Coronavirus US Data
davegoblue
10/1/2020
library(tidyverse)## -- Attaching packages --------------------------------------- tidyverse 1.3.0 --## v ggplot2 3.3.2 v purrr 0.3.4
## v tibble 3.0.4 v dplyr 1.0.2
## v tidyr 1.1.2 v stringr 1.4.0
## v readr 1.4.0 v forcats 0.5.0## -- Conflicts ------------------------------------------ tidyverse_conflicts() --
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()Background
This file is for analysis of several coronavirus data sources focused on the US in 2020. Data include areas such as cases, hospitalizations, and deaths tracked to cornavirus, as well as all-cause deaths.
The previous version of this file includes many exploratory analysis components. This version is designed with the following portions of the previous code:
- Functions
- Run key analyses
- Save and load key files
COVID Tracking Project
The first section is for analysis of data from The COVID Tracking Project. This file contains data on positive tests, hospitalizations, deaths, and the like for coronavirus cases in the US. Downloaded data are unique by state and date.
Key Functions (COVID Tracking Project, USA Facts, CDC All-Cause Deaths)
Functions for working with data from the COVID Tracking Project, USA Facts, and the CDC All-Cause Death data are available in a separate file which is sourced below:
source("./Coronavirus_Statistics_Functions_v002.R")Running Key Analyses (COVID Tracking Project)
The data from COVID Tracking Project can be loaded and analyzed using the functions above and a variable mapping file:
# STEP 0: Create a variable mapping file
varMapper <- c("cases"="Cases",
"newCases"="Increase in cases, most recent 30 days",
"casesroll7"="Rolling 7-day mean cases",
"deaths"="Deaths",
"newDeaths"="Increase in deaths, most recent 30 days",
"deathsroll7"="Rolling 7-day mean deaths",
"cpm"="Cases per million",
"cpm7"="Cases per day (7-day rolling mean) per million",
"newcpm"="Increase in cases, most recent 30 days, per million",
"dpm"="Deaths per million",
"dpm7"="Deaths per day (7-day rolling mean) per million",
"newdpm"="Increase in deaths, most recent 30 days, per million",
"hpm7"="Currently Hospitalized per million (7-day rolling mean)",
"tpm"="Tests per million",
"tpm7"="Tests per million per day (7-day rolling mean)"
)
# Test function for hierarchical clustering with Vermont reassigned to New Hampshire
test_hier5 <- readRunCOVIDTrackingProject(thruLabel="Aug 20, 2020",
readFrom="./RInputFiles/Coronavirus/CV_downloaded_200820.csv",
hierarchical=TRUE,
kCut=6,
reAssignState=list("VT"="NH"),
minShape=3,
ratioDeathvsCase = 5,
ratioTotalvsShape = 0.5,
minDeath=100,
minCase=10000
)## Parsed with column specification:
## cols(
## .default = col_double(),
## state = col_character(),
## dataQualityGrade = col_character(),
## lastUpdateEt = col_character(),
## dateModified = col_datetime(format = ""),
## checkTimeEt = col_character(),
## dateChecked = col_datetime(format = ""),
## hash = col_character(),
## grade = col_logical()
## )## See spec(...) for full column specifications.##
## File is unique by state and date
##
##
## Overall control totals in file:
## # A tibble: 1 x 3
## positiveIncrease deathIncrease hospitalizedCurrently
## <dbl> <dbl> <dbl>
## 1 5546056 166127 6486294
## Observations: 9,449
## Variables: 53
## $ date <date> 2020-08-20, 2020-08-20, 2020-08-20, 20...
## $ state <chr> "AK", "AL", "AR", "AS", "AZ", "CA", "CO...
## $ positive <dbl> 5332, 112449, 54765, 0, 196280, 644751,...
## $ negative <dbl> 307315, 784330, 593744, 1514, 922163, 9...
## $ pending <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,...
## $ hospitalizedCurrently <dbl> 51, 1105, 499, NA, 1070, 6212, 238, 47,...
## $ hospitalizedCumulative <dbl> NA, 13330, 3790, NA, 21143, NA, 6784, 1...
## $ inIcuCurrently <dbl> NA, NA, NA, NA, 388, 1707, NA, NA, 26, ...
## $ inIcuCumulative <dbl> NA, 1348, NA, NA, NA, NA, NA, NA, NA, N...
## $ onVentilatorCurrently <dbl> 6, NA, 108, NA, 233, NA, NA, NA, 12, NA...
## $ onVentilatorCumulative <dbl> NA, 734, 488, NA, NA, NA, NA, NA, NA, N...
## $ recovered <dbl> 1513, 44684, 48458, NA, 28471, NA, 5759...
## $ dataQualityGrade <chr> "A", "B", "A", "C", "A+", "B", "A", "B"...
## $ lastUpdateEt <chr> "8/20/2020 0:00", "8/20/2020 11:00", "8...
## $ dateModified <dttm> 2020-08-20 00:00:00, 2020-08-20 11:00:...
## $ checkTimeEt <chr> "8/19/2020 20:00", "8/20/2020 7:00", "8...
## $ death <dbl> 29, 1974, 641, 0, 4684, 11686, 1800, 44...
## $ hospitalized <dbl> NA, 13330, 3790, NA, 21143, NA, 6784, 1...
## $ dateChecked <dttm> 2020-08-20 00:00:00, 2020-08-20 11:00:...
## $ totalTestsViral <dbl> 312647, 891813, 648509, NA, 1116897, 10...
## $ positiveTestsViral <dbl> 4970, NA, NA, NA, NA, NA, NA, NA, NA, N...
## $ negativeTestsViral <dbl> 307360, NA, 593744, NA, NA, NA, NA, NA,...
## $ positiveCasesViral <dbl> 5332, 107483, 54765, 0, 194734, 644751,...
## $ deathConfirmed <dbl> 29, 1905, NA, NA, 4429, NA, NA, 3572, N...
## $ deathProbable <dbl> NA, 69, NA, NA, 255, NA, NA, 886, NA, 6...
## $ totalTestEncountersViral <dbl> NA, NA, NA, NA, NA, NA, 895207, NA, NA,...
## $ totalTestsPeopleViral <dbl> NA, NA, NA, 1514, NA, NA, 645170, NA, 2...
## $ totalTestsAntibody <dbl> NA, NA, NA, NA, 255456, NA, 150931, NA,...
## $ positiveTestsAntibody <dbl> NA, NA, NA, NA, NA, NA, 10406, NA, NA, ...
## $ negativeTestsAntibody <dbl> NA, NA, NA, NA, NA, NA, 140525, NA, NA,...
## $ totalTestsPeopleAntibody <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,...
## $ positiveTestsPeopleAntibody <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,...
## $ negativeTestsPeopleAntibody <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,...
## $ totalTestsPeopleAntigen <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,...
## $ positiveTestsPeopleAntigen <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,...
## $ totalTestsAntigen <dbl> NA, NA, 10358, NA, NA, NA, NA, NA, NA, ...
## $ positiveTestsAntigen <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,...
## $ fips <dbl> 2, 1, 5, 60, 4, 6, 8, 9, 11, 10, 12, 13...
## $ positiveIncrease <dbl> 85, 971, 549, 0, 723, 5920, 270, 118, 5...
## $ negativeIncrease <dbl> 1713, 10462, 6680, 0, 6481, 81363, 4657...
## $ total <dbl> 312647, 896779, 648509, 1514, 1118443, ...
## $ totalTestResults <dbl> 312647, 896779, 648509, 1514, 1118443, ...
## $ totalTestResultsIncrease <dbl> 1798, 11433, 7229, 0, 7204, 87283, 7348...
## $ posNeg <dbl> 312647, 896779, 648509, 1514, 1118443, ...
## $ deathIncrease <dbl> 0, 30, 10, 0, 50, 163, 12, 1, 1, 0, 119...
## $ hospitalizedIncrease <dbl> 0, 250, 47, 0, 123, 0, 3, 72, 0, 0, 450...
## $ hash <chr> "c83a1d575a597788adccbe170950b8d197754b...
## $ commercialScore <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...
## $ negativeRegularScore <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...
## $ negativeScore <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...
## $ positiveScore <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...
## $ score <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...
## $ grade <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,...
##
##
## Control totals - note that validState other than TRUE will be discarded
##
## # A tibble: 2 x 6
## validState cases deaths hosp tests n
## <lgl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 FALSE 29761 385 NA 392743 790
## 2 TRUE 5516295 165742 NA 69638073 8659
## Observations: 8,659
## Variables: 6
## $ date <date> 2020-08-20, 2020-08-20, 2020-08-20, 2020-08-20, 2020-08-20,...
## $ state <chr> "AK", "AL", "AR", "AZ", "CA", "CO", "CT", "DC", "DE", "FL", ...
## $ cases <dbl> 85, 971, 549, 723, 5920, 270, 118, 55, 75, 4555, 2759, 235, ...
## $ deaths <dbl> 0, 30, 10, 50, 163, 12, 1, 1, 0, 119, 55, 3, 8, 9, 27, 11, 0...
## $ hosp <dbl> 51, 1105, 499, 1070, 6212, 238, 47, 78, 40, 5067, 2506, 187,...
## $ tests <dbl> 1798, 11433, 7229, 7204, 87283, 7348, 12420, 2326, 2087, 295...
## Observations: 8,659
## Variables: 14
## $ date <date> 2020-01-22, 2020-01-23, 2020-01-24, 2020-01-25, 2020-01-26,...
## $ state <chr> "WA", "WA", "WA", "WA", "WA", "WA", "WA", "WA", "WA", "WA", ...
## $ cases <dbl> 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 2, 1, 1, 0, 3, 1, 1, 0, 0, ...
## $ deaths <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...
## $ hosp <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, ...
## $ tests <dbl> 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 2, 1, 1, 0, 3, 1, 1, 0, 0, ...
## $ cpm <dbl> 0.0000000, 0.0000000, 0.0000000, 0.0000000, 0.0000000, 0.000...
## $ dpm <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...
## $ hpm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, ...
## $ tpm <dbl> 0.0000000, 0.0000000, 0.0000000, 0.0000000, 0.0000000, 0.000...
## $ cpm7 <dbl> NA, NA, NA, 0.00000000, 0.01992331, 0.01992331, 0.01992331, ...
## $ dpm7 <dbl> NA, NA, NA, 0.00000000, 0.00000000, 0.00000000, 0.00000000, ...
## $ hpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, ...
## $ tpm7 <dbl> NA, NA, NA, 0.00000000, 0.01992331, 0.01992331, 0.01992331, ...
##
## Recency is defined as 2020-07-22 through current
##
## Recency is defined as 2020-07-22 through current
# Test function for k-means clustering using the per capita data file previously created
test_km5 <- readRunCOVIDTrackingProject(thruLabel="Aug 20, 2020",
dfPerCapita=test_hier5$dfPerCapita,
hierarchical=FALSE,
makeCumulativePlots=FALSE,
minShape=3,
ratioDeathvsCase = 5,
ratioTotalvsShape = 0.5,
minDeath=100,
minCase=10000,
nCenters=5,
testCenters=1:10,
iter.max=20,
nstart=10,
seed=2008261400
)
##
## Cluster means and counts
## 1 2 3 4 5
## . 10.00 14.00 9.00 14.00 4.00
## totalCases 0.28 0.86 0.71 0.50 0.78
## totalDeaths 0.41 1.42 2.73 0.99 5.32
## cases_3 0.03 0.01 0.05 0.02 0.10
## deaths_3 0.27 0.08 0.13 0.06 0.15
## cases_4 0.07 0.06 0.26 0.12 0.50
## deaths_4 1.27 0.63 1.73 1.00 2.58
## cases_5 0.06 0.07 0.24 0.19 0.23
## deaths_5 0.69 0.66 1.77 1.50 1.59
## cases_6 0.08 0.18 0.11 0.15 0.07
## deaths_6 0.42 0.61 0.70 1.01 0.44
## cases_7 0.23 0.44 0.21 0.28 0.06
## deaths_7 0.70 1.60 0.42 0.81 0.18
## cases_8 0.16 0.22 0.14 0.19 0.04
## deaths_8 0.70 1.38 0.25 0.61 0.06
##
## Recency is defined as 2020-07-22 through current
##
## Recency is defined as 2020-07-22 through current
# Run in a session that has access to the old data files
# identical(test_hier5$useClusters, clustVec) # TRUE
# identical(test_km5$useClusters, testCluster_km5$objCluster$cluster) # TRUECluster files produced using the new functions and the existing August 20, 2020 data are identical to ‘clustVec’ and ‘testCluster_km5’ produced in the _v001 code.
Further, new data is downloaded that is current through September 30, 2020, and the test_hier5 clusters are used for analysis:
# Test function for hierarchical clustering with Vermont reassigned to New Hampshire
locDownload <- "./RInputFiles/Coronavirus/CV_downloaded_201001.csv"
test_hier5_201001 <- readRunCOVIDTrackingProject(thruLabel="Sep 30, 2020",
readFrom=locDownload,
compareFile=test_hier5$dfRaw,
useClusters=test_hier5$useClusters
)## Parsed with column specification:
## cols(
## .default = col_double(),
## state = col_character(),
## dataQualityGrade = col_character(),
## lastUpdateEt = col_character(),
## dateModified = col_datetime(format = ""),
## checkTimeEt = col_character(),
## dateChecked = col_datetime(format = ""),
## fips = col_character(),
## totalTestResultsSource = col_character(),
## hash = col_character(),
## grade = col_logical()
## )## See spec(...) for full column specifications.##
## File is unique by state and date
##
##
## Overall control totals in file:
## # A tibble: 1 x 3
## positiveIncrease deathIncrease hospitalizedCurrently
## <dbl> <dbl> <dbl>
## 1 7198509 198929 7830770
##
## *** COMPARISONS TO REFERENCE FILE: compareFile
##
## Checkin for similarity of: column names
## In reference but not in current:
## In current but not in reference: totalTestResultsSource
##
## Checkin for similarity of: states
## In reference but not in current:
## In current but not in reference:
##
## Checkin for similarity of: dates
## In reference but not in current:
## In current but not in reference: 2020-09-30 2020-09-29 2020-09-28 2020-09-27 2020-09-26 2020-09-25 2020-09-24 2020-09-23 2020-09-22 2020-09-21 2020-09-20 2020-09-19 2020-09-18 2020-09-17 2020-09-16 2020-09-15 2020-09-14 2020-09-13 2020-09-12 2020-09-11 2020-09-10 2020-09-09 2020-09-08 2020-09-07 2020-09-06 2020-09-05 2020-09-04 2020-09-03 2020-09-02 2020-09-01 2020-08-31 2020-08-30 2020-08-29 2020-08-28 2020-08-27 2020-08-26 2020-08-25 2020-08-24 2020-08-23 2020-08-22 2020-08-21
##
## *** Difference of at least 5 and difference is at least 1%:## Joining, by = c("date", "name")## date name newValue oldValue
## 1 2020-02-15 positiveIncrease 0 7
## 2 2020-02-16 positiveIncrease 0 7
## 3 2020-02-17 positiveIncrease 0 15
## 4 2020-02-18 positiveIncrease 0 9
## 5 2020-02-19 positiveIncrease 0 10
## 6 2020-02-20 positiveIncrease 0 13
## 7 2020-02-21 positiveIncrease 0 11
## 8 2020-02-22 positiveIncrease 0 13
## 9 2020-02-23 positiveIncrease 0 16
## 10 2020-02-24 positiveIncrease 0 26
## 11 2020-02-25 positiveIncrease 0 31
## 12 2020-02-26 positiveIncrease 0 29
## 13 2020-02-27 positiveIncrease 0 27
## 14 2020-02-28 positiveIncrease 0 40
## 15 2020-02-29 positiveIncrease 18 24
## 16 2020-03-01 positiveIncrease 16 78
## 17 2020-03-02 positiveIncrease 44 82
## 18 2020-03-03 positiveIncrease 48 100
## 19 2020-03-04 positiveIncrease 62 110
## 20 2020-03-05 positiveIncrease 103 151
## 21 2020-03-06 positiveIncrease 108 137
## 22 2020-03-07 positiveIncrease 175 217
## 23 2020-03-08 positiveIncrease 198 267
## 24 2020-03-09 positiveIncrease 292 367
## 25 2020-03-10 positiveIncrease 387 441
## 26 2020-03-11 positiveIncrease 509 527
## 27 2020-03-13 positiveIncrease 1072 1025
## 28 2020-03-15 positiveIncrease 1291 1251
## 29 2020-03-16 positiveIncrease 1739 1560
## 30 2020-03-17 positiveIncrease 2588 3613
## 31 2020-03-18 positiveIncrease 3089 3171
## 32 2020-03-20 positiveIncrease 6147 6255
## 33 2020-03-21 positiveIncrease 6793 6885
## 34 2020-03-22 positiveIncrease 9125 9259
## 35 2020-03-24 positiveIncrease 10769 10632
## 36 2020-04-03 hospitalizedCurrently 25729 25472
## 37 2020-04-29 positiveIncrease 26168 26641
## 38 2020-04-30 positiveIncrease 29975 29568
## 39 2020-05-08 positiveIncrease 27222 27605
## 40 2020-05-09 positiveIncrease 25218 24810
## 41 2020-05-10 positiveIncrease 21055 21504
## 42 2020-05-12 positiveIncrease 22890 22663
## 43 2020-05-26 positiveIncrease 16825 16629
## 44 2020-06-08 positiveIncrease 17209 17012
## 45 2020-06-19 positiveIncrease 31471 31046
## 46 2020-06-21 positiveIncrease 27928 27284
## 47 2020-06-23 positiveIncrease 33447 33021
## 48 2020-06-24 positiveIncrease 39075 38684
## 49 2020-06-25 positiveIncrease 39637 39072
## 50 2020-06-27 positiveIncrease 43164 43783
## 51 2020-06-29 positiveIncrease 39813 39175
## 52 2020-07-26 positiveIncrease 61000 61713
## 53 2020-07-28 positiveIncrease 59003 56229
## 54 2020-07-29 positiveIncrease 64408 66969
## 55 2020-08-02 positiveIncrease 46812 48266
## 56 2020-08-09 positiveIncrease 50627 51365
## 57 2020-08-16 positiveIncrease 42487 43083
## 58 2020-08-20 positiveIncrease 43758 43245
## 59 2020-08-20 deathIncrease 1134 1117## Joining, by = c("date", "name")##
##
## *** Difference of at least 5 and difference is at least 1%:## Joining, by = c("state", "name")
## state name newValue oldValue
## 1 WA positiveIncrease 70973 68687
## 2 WI hospitalizedCurrently 45951 51401
## Observations: 11,802
## Variables: 54
## $ date <date> 2020-09-30, 2020-09-30, 2020-09-30, 20...
## $ state <chr> "AK", "AL", "AR", "AS", "AZ", "CA", "CO...
## $ positive <dbl> 8780, 154701, 83697, 0, 218507, 810625,...
## $ negative <dbl> 448427, 994475, 949107, 1571, 1243641, ...
## $ pending <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,...
## $ totalTestResults <dbl> 457207, 1132039, 1029717, 1571, 1457575...
## $ hospitalizedCurrently <dbl> 53, 776, 484, NA, 560, 3267, 268, 104, ...
## $ hospitalizedCumulative <dbl> NA, 17257, 5354, NA, 22119, NA, 7558, 1...
## $ inIcuCurrently <dbl> NA, NA, 218, NA, 115, 830, NA, NA, 28, ...
## $ inIcuCumulative <dbl> NA, 1815, NA, NA, NA, NA, NA, NA, NA, N...
## $ onVentilatorCurrently <dbl> 7, NA, 93, NA, 55, NA, NA, NA, 9, NA, N...
## $ onVentilatorCumulative <dbl> NA, 1025, 676, NA, NA, NA, NA, NA, NA, ...
## $ recovered <dbl> 4555, 67948, 75312, NA, 35261, NA, 6500...
## $ dataQualityGrade <chr> "A", "A", "A+", "D", "A+", "B", "A", "B...
## $ lastUpdateEt <chr> "9/30/2020 03:59", "9/30/2020 11:00", "...
## $ dateModified <dttm> 2020-09-30 03:59:00, 2020-09-30 11:00:...
## $ checkTimeEt <chr> "09/29 23:59", "09/30 07:00", "09/29 20...
## $ death <dbl> 56, 2540, 1369, 0, 5650, 15792, 1952, 4...
## $ hospitalized <dbl> NA, 17257, 5354, NA, 22119, NA, 7558, 1...
## $ dateChecked <dttm> 2020-09-30 03:59:00, 2020-09-30 11:00:...
## $ totalTestsViral <dbl> 457207, 1132039, 1029717, 1571, NA, 147...
## $ positiveTestsViral <dbl> 7807, NA, NA, NA, NA, NA, NA, NA, NA, N...
## $ negativeTestsViral <dbl> 449109, NA, 949107, NA, NA, NA, NA, NA,...
## $ positiveCasesViral <dbl> 8780, 137564, 80610, 0, 213934, 810625,...
## $ deathConfirmed <dbl> 56, 2399, 1223, NA, 5382, NA, NA, 3610,...
## $ deathProbable <dbl> NA, 141, 146, NA, 268, NA, NA, 898, NA,...
## $ totalTestEncountersViral <dbl> NA, NA, NA, NA, NA, NA, 1341180, NA, 38...
## $ totalTestsPeopleViral <dbl> NA, NA, NA, NA, 1457575, NA, 902242, NA...
## $ totalTestsAntibody <dbl> NA, NA, NA, NA, 293098, NA, 167989, NA,...
## $ positiveTestsAntibody <dbl> NA, NA, NA, NA, NA, NA, 11733, NA, NA, ...
## $ negativeTestsAntibody <dbl> NA, NA, NA, NA, NA, NA, 156256, NA, NA,...
## $ totalTestsPeopleAntibody <dbl> NA, 58393, NA, NA, NA, NA, NA, NA, NA, ...
## $ positiveTestsPeopleAntibody <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,...
## $ negativeTestsPeopleAntibody <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,...
## $ totalTestsPeopleAntigen <dbl> NA, NA, 11700, NA, NA, NA, NA, NA, NA, ...
## $ positiveTestsPeopleAntigen <dbl> NA, NA, 3383, NA, NA, NA, NA, NA, NA, N...
## $ totalTestsAntigen <dbl> NA, NA, 21856, NA, NA, NA, NA, NA, NA, ...
## $ positiveTestsAntigen <dbl> NA, NA, 3300, NA, NA, NA, NA, NA, NA, N...
## $ fips <chr> "02", "01", "05", "60", "04", "06", "08...
## $ positiveIncrease <dbl> 106, 1147, 942, 0, 323, 3200, 535, 221,...
## $ negativeIncrease <dbl> 6045, 11312, 21205, 0, 3735, 88457, 674...
## $ total <dbl> 457207, 1149176, 1032804, 1571, 1462148...
## $ totalTestResultsSource <chr> "posNeg", "posNeg", "posNeg", "posNeg",...
## $ totalTestResultsIncrease <dbl> 6151, 12327, 21812, 0, 4047, 91657, 145...
## $ posNeg <dbl> 457207, 1149176, 1032804, 1571, 1462148...
## $ deathIncrease <dbl> 0, 23, 19, 0, 18, 152, 7, 3, 1, 1, 175,...
## $ hospitalizedIncrease <dbl> 0, 75, 0, 0, 72, 0, 28, 0, 0, 0, 253, 1...
## $ hash <chr> "1716aba4b705ff74814829d25487c6e57d2712...
## $ commercialScore <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...
## $ negativeRegularScore <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...
## $ negativeScore <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...
## $ positiveScore <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...
## $ score <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...
## $ grade <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,...
##
##
## Control totals - note that validState other than TRUE will be discarded
##
## # A tibble: 2 x 6
## validState cases deaths hosp tests n
## <lgl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 FALSE 52622 732 NA 442114 995
## 2 TRUE 7145887 198197 NA 103496519 10807
## Observations: 10,807
## Variables: 6
## $ date <date> 2020-09-30, 2020-09-30, 2020-09-30, 2020-09-30, 2020-09-30,...
## $ state <chr> "AK", "AL", "AR", "AZ", "CA", "CO", "CT", "DC", "DE", "FL", ...
## $ cases <dbl> 106, 1147, 942, 323, 3200, 535, 221, 26, 82, 1948, 1720, 87,...
## $ deaths <dbl> 0, 23, 19, 18, 152, 7, 3, 1, 1, 175, 27, 0, 16, 4, 35, 20, 4...
## $ hosp <dbl> 53, 776, 484, 560, 3267, 268, 104, 95, 72, 2097, 1896, 147, ...
## $ tests <dbl> 6151, 12327, 21812, 4047, 91657, 14538, 12401, 1464, 1074, 1...
## Observations: 10,807
## Variables: 14
## $ date <date> 2020-01-22, 2020-01-22, 2020-01-23, 2020-01-23, 2020-01-24,...
## $ state <chr> "MA", "WA", "MA", "WA", "MA", "WA", "MA", "WA", "MA", "WA", ...
## $ cases <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...
## $ deaths <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...
## $ hosp <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, ...
## $ tests <dbl> 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, ...
## $ cpm <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...
## $ dpm <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...
## $ hpm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, ...
## $ tpm <dbl> 0.0000000, 0.0000000, 0.1471796, 0.0000000, 0.0000000, 0.000...
## $ cpm7 <dbl> NA, NA, NA, NA, NA, NA, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...
## $ dpm7 <dbl> NA, NA, NA, NA, NA, NA, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...
## $ hpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, ...
## $ tpm7 <dbl> NA, NA, NA, NA, NA, NA, 0.04205130, 0.00000000, 0.06307695, ...
##
## Recency is defined as 2020-09-01 through current
##
## Recency is defined as 2020-09-01 through current
Segments appear to be on trend with previous analysis, with what was previously called “late pandemic” having peaked and the segments that had smaller deaths per million showing higher percentage increases.
USA Facts
The second section is for analysis of data from USA Facts. This site contains county-level data for the US focused on coronavirus cases and deaths. Data are provided with one row per county and one column for each date, and are thus unique by county. There are differences in state and US totals for coronavirus cases and deaths when reported by COVID Tracking Project and USA Facts, though trends, timing, and relative magnitudes are generally in agreement between the two sources.
Key Functions (USA Facts)
Functions for reading and analyzing data from USA Facts are sourced above from Coronavirus_Statistics_Functions_v002.R
Running Key Analyses (USA Facts)
The data from USA Facts can be loaded and analyzed using the sourced functions, a variable mapping file, and a main function that calls the other functions and returns a list:
# STEP 1a: Define the locations for the population, cases, and deaths file
popFile <- "./RInputFiles/Coronavirus/covid_county_population_usafacts.csv"
caseFile_20200903 <- "./RInputFiles/Coronavirus/covid_confirmed_usafacts_downloaded_20200903.csv"
deathFile_20200903 <- "./RInputFiles/Coronavirus/covid_deaths_usafacts_downloaded_20200903.csv"
maxDate_20200903 <- "2020-08-31"
# STEP 1b: Read and pivot data from USA Facts; extract population data file as pop_usafacts
rawUSAFacts_20200903 <- readPivotUSAFacts(popFile=popFile,
caseFile=caseFile_20200903,
deathFile=deathFile_20200903,
unassignedDate=maxDate_20200903
)## Parsed with column specification:
## cols(
## countyFIPS = col_double(),
## `County Name` = col_character(),
## State = col_character(),
## population = col_double()
## )## Parsed with column specification:
## cols(
## .default = col_double(),
## `County Name` = col_character(),
## State = col_character()
## )## See spec(...) for full column specifications.## Parsed with column specification:
## cols(
## .default = col_double(),
## `County Name` = col_character(),
## State = col_character()
## )## See spec(...) for full column specifications.## Joining, by = c("state", "date", "unassigned")
pop_usafacts <- rawUSAFacts_20200903$pop
# STEP 2: Read case and death data (redundant), combine, and add population totals; no clusters by default
burden_20200903_new <- readUSAFacts(
caseFile=caseFile_20200903,
deathFile=deathFile_20200903,
stateClusters=NULL
)## Parsed with column specification:
## cols(
## .default = col_double(),
## `County Name` = col_character(),
## State = col_character()
## )## See spec(...) for full column specifications.## Observations: 3,195
## Variables: 228
## $ countyFIPS <dbl> 0, 1001, 1003, 1005, 1007, 1009, 1011, 1013, 1015, 10...
## $ `County Name` <chr> "Statewide Unallocated", "Autauga County", "Baldwin C...
## $ State <chr> "AL", "AL", "AL", "AL", "AL", "AL", "AL", "AL", "AL",...
## $ stateFIPS <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,...
## $ `1/22/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `1/23/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `1/24/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `1/25/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `1/26/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `1/27/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `1/28/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `1/29/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `1/30/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `1/31/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `2/1/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `2/2/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `2/3/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `2/4/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `2/5/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `2/6/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `2/7/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `2/8/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `2/9/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `2/10/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `2/11/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `2/12/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `2/13/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `2/14/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `2/15/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `2/16/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `2/17/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `2/18/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `2/19/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `2/20/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `2/21/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `2/22/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `2/23/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `2/24/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `2/25/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `2/26/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `2/27/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `2/28/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `2/29/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `3/1/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `3/2/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `3/3/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `3/4/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `3/5/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `3/6/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `3/7/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `3/8/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `3/9/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `3/10/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `3/11/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `3/12/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `3/13/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `3/14/20` <dbl> 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `3/15/20` <dbl> 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `3/16/20` <dbl> 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `3/17/20` <dbl> 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `3/18/20` <dbl> 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `3/19/20` <dbl> 0, 0, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `3/20/20` <dbl> 0, 0, 2, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `3/21/20` <dbl> 0, 0, 2, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `3/22/20` <dbl> 0, 0, 3, 0, 0, 0, 0, 0, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `3/23/20` <dbl> 0, 0, 3, 0, 0, 0, 0, 0, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `3/24/20` <dbl> 0, 1, 4, 0, 0, 0, 0, 0, 2, 5, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `3/25/20` <dbl> 0, 4, 4, 0, 0, 1, 0, 1, 2, 10, 1, 1, 0, 0, 1, 1, 0, 1...
## $ `3/26/20` <dbl> 0, 6, 5, 0, 0, 2, 2, 1, 2, 13, 1, 4, 1, 0, 1, 1, 0, 1...
## $ `3/27/20` <dbl> 0, 6, 5, 0, 0, 5, 2, 1, 3, 15, 1, 7, 1, 0, 1, 3, 0, 1...
## $ `3/28/20` <dbl> 0, 6, 10, 0, 0, 5, 2, 1, 3, 17, 1, 7, 1, 0, 2, 4, 0, ...
## $ `3/29/20` <dbl> 0, 6, 15, 0, 0, 5, 2, 1, 3, 27, 2, 8, 1, 0, 2, 5, 0, ...
## $ `3/30/20` <dbl> 0, 7, 18, 0, 2, 5, 2, 1, 9, 36, 2, 10, 2, 0, 2, 5, 0,...
## $ `3/31/20` <dbl> 0, 7, 19, 0, 3, 5, 2, 1, 9, 36, 2, 11, 3, 0, 2, 5, 0,...
## $ `4/1/20` <dbl> 0, 10, 23, 0, 3, 5, 2, 1, 11, 45, 2, 13, 4, 2, 3, 6, ...
## $ `4/2/20` <dbl> 0, 10, 25, 0, 4, 6, 2, 1, 12, 67, 4, 14, 4, 2, 7, 6, ...
## $ `4/3/20` <dbl> 0, 12, 28, 1, 4, 9, 2, 1, 20, 81, 5, 15, 4, 3, 8, 7, ...
## $ `4/4/20` <dbl> 0, 12, 29, 2, 4, 10, 2, 1, 21, 87, 6, 15, 4, 7, 9, 7,...
## $ `4/5/20` <dbl> 0, 12, 34, 2, 7, 10, 2, 1, 24, 90, 6, 18, 5, 9, 9, 7,...
## $ `4/6/20` <dbl> 0, 12, 38, 3, 7, 10, 2, 1, 38, 96, 6, 20, 6, 9, 9, 9,...
## $ `4/7/20` <dbl> 0, 12, 42, 3, 8, 10, 2, 2, 48, 102, 6, 20, 6, 10, 9, ...
## $ `4/8/20` <dbl> 0, 12, 49, 3, 9, 10, 3, 3, 52, 140, 7, 22, 6, 10, 11,...
## $ `4/9/20` <dbl> 0, 17, 59, 7, 11, 11, 4, 3, 54, 161, 7, 25, 6, 13, 11...
## $ `4/10/20` <dbl> 0, 17, 59, 9, 11, 12, 4, 3, 54, 171, 7, 27, 7, 13, 11...
## $ `4/11/20` <dbl> 0, 19, 66, 10, 13, 12, 4, 6, 57, 184, 7, 30, 9, 15, 1...
## $ `4/12/20` <dbl> 0, 19, 71, 10, 16, 13, 4, 7, 60, 200, 9, 30, 10, 19, ...
## $ `4/13/20` <dbl> 0, 19, 78, 10, 17, 15, 6, 8, 61, 212, 9, 33, 10, 19, ...
## $ `4/14/20` <dbl> 0, 23, 87, 11, 17, 16, 8, 8, 62, 216, 9, 33, 12, 21, ...
## $ `4/15/20` <dbl> 0, 25, 98, 13, 19, 17, 8, 11, 62, 227, 10, 37, 13, 22...
## $ `4/16/20` <dbl> 0, 25, 102, 14, 23, 18, 8, 11, 63, 234, 11, 37, 13, 2...
## $ `4/17/20` <dbl> 0, 25, 103, 15, 23, 20, 8, 13, 63, 236, 12, 37, 13, 2...
## $ `4/18/20` <dbl> 0, 25, 109, 18, 26, 20, 9, 13, 66, 240, 12, 39, 14, 2...
## $ `4/19/20` <dbl> 0, 27, 114, 20, 28, 21, 9, 14, 72, 246, 12, 42, 14, 2...
## $ `4/20/20` <dbl> 0, 28, 117, 22, 32, 22, 11, 14, 80, 257, 12, 43, 17, ...
## $ `4/21/20` <dbl> 0, 30, 123, 28, 32, 26, 11, 15, 83, 259, 12, 44, 18, ...
## $ `4/22/20` <dbl> 0, 32, 132, 29, 33, 29, 11, 17, 85, 270, 12, 46, 21, ...
## $ `4/23/20` <dbl> 0, 33, 143, 30, 33, 31, 12, 19, 88, 275, 12, 47, 22, ...
## $ `4/24/20` <dbl> 0, 36, 147, 32, 34, 31, 12, 21, 89, 282, 12, 49, 25, ...
## $ `4/25/20` <dbl> 0, 37, 154, 33, 35, 31, 12, 28, 90, 284, 12, 49, 27, ...
## $ `4/26/20` <dbl> 0, 37, 161, 33, 38, 34, 12, 32, 90, 285, 14, 51, 32, ...
## $ `4/27/20` <dbl> 0, 39, 168, 35, 42, 34, 12, 34, 90, 289, 14, 51, 39, ...
## $ `4/28/20` <dbl> 0, 40, 171, 37, 42, 34, 12, 45, 92, 290, 15, 52, 39, ...
## $ `4/29/20` <dbl> 0, 42, 173, 37, 42, 36, 12, 51, 93, 290, 15, 52, 39, ...
## $ `4/30/20` <dbl> 0, 42, 174, 39, 42, 37, 13, 53, 93, 290, 15, 52, 43, ...
## $ `5/1/20` <dbl> 0, 42, 175, 42, 42, 39, 14, 65, 93, 290, 15, 52, 49, ...
## $ `5/2/20` <dbl> 0, 45, 181, 43, 42, 40, 14, 92, 98, 294, 15, 54, 49, ...
## $ `5/3/20` <dbl> 0, 48, 187, 45, 42, 40, 14, 105, 105, 300, 16, 57, 49...
## $ `5/4/20` <dbl> 0, 53, 188, 45, 42, 40, 16, 114, 105, 302, 16, 58, 51...
## $ `5/5/20` <dbl> 0, 53, 189, 47, 43, 40, 18, 120, 114, 304, 17, 60, 54...
## $ `5/6/20` <dbl> 0, 58, 196, 47, 43, 42, 18, 130, 114, 306, 18, 61, 54...
## $ `5/7/20` <dbl> 0, 61, 205, 51, 44, 44, 18, 155, 120, 308, 18, 63, 56...
## $ `5/8/20` <dbl> 0, 67, 208, 53, 44, 44, 21, 162, 123, 311, 21, 63, 59...
## $ `5/9/20` <dbl> 0, 68, 216, 58, 45, 44, 22, 178, 124, 314, 22, 64, 61...
## $ `5/10/20` <dbl> 0, 74, 222, 59, 46, 44, 23, 189, 124, 316, 22, 65, 66...
## $ `5/11/20` <dbl> 0, 84, 224, 61, 46, 45, 26, 196, 125, 319, 24, 67, 67...
## $ `5/12/20` <dbl> 0, 91, 227, 67, 46, 45, 26, 224, 126, 324, 24, 69, 69...
## $ `5/13/20` <dbl> 0, 93, 231, 69, 46, 45, 28, 230, 127, 324, 24, 73, 72...
## $ `5/14/20` <dbl> 0, 103, 243, 74, 46, 45, 28, 249, 128, 326, 25, 74, 7...
## $ `5/15/20` <dbl> 0, 103, 244, 79, 49, 45, 32, 258, 129, 326, 26, 75, 8...
## $ `5/16/20` <dbl> 0, 110, 254, 79, 50, 45, 35, 271, 130, 328, 27, 77, 8...
## $ `5/17/20` <dbl> 0, 110, 254, 81, 50, 46, 35, 272, 130, 328, 27, 77, 8...
## $ `5/18/20` <dbl> 0, 120, 260, 85, 50, 47, 40, 285, 133, 329, 28, 79, 8...
## $ `5/19/20` <dbl> 0, 127, 262, 90, 51, 47, 52, 295, 133, 329, 29, 80, 9...
## $ `5/20/20` <dbl> 0, 136, 270, 96, 52, 47, 64, 312, 136, 330, 30, 83, 1...
## $ `5/21/20` <dbl> 0, 147, 270, 100, 52, 48, 71, 321, 136, 330, 31, 84, ...
## $ `5/22/20` <dbl> 0, 149, 271, 104, 55, 49, 89, 329, 137, 330, 33, 85, ...
## $ `5/23/20` <dbl> 0, 155, 273, 105, 58, 49, 105, 335, 138, 330, 33, 86,...
## $ `5/24/20` <dbl> 0, 159, 274, 110, 59, 49, 111, 344, 141, 336, 33, 87,...
## $ `5/25/20` <dbl> 0, 173, 276, 116, 62, 49, 141, 368, 147, 337, 33, 87,...
## $ `5/26/20` <dbl> 0, 189, 277, 122, 66, 51, 167, 380, 150, 338, 33, 90,...
## $ `5/27/20` <dbl> 0, 192, 281, 130, 71, 53, 176, 391, 152, 340, 33, 93,...
## $ `5/28/20` <dbl> 0, 205, 281, 132, 71, 58, 185, 392, 152, 349, 34, 97,...
## $ `5/29/20` <dbl> 0, 212, 282, 147, 71, 60, 201, 396, 153, 352, 36, 99,...
## $ `5/30/20` <dbl> 0, 216, 283, 150, 72, 61, 203, 402, 154, 353, 37, 100...
## $ `5/31/20` <dbl> 0, 220, 288, 164, 75, 62, 209, 410, 157, 355, 37, 100...
## $ `6/1/20` <dbl> 0, 233, 292, 172, 76, 63, 209, 414, 164, 358, 38, 103...
## $ `6/2/20` <dbl> 0, 238, 292, 175, 76, 63, 212, 416, 165, 358, 38, 104...
## $ `6/3/20` <dbl> 0, 239, 292, 177, 76, 63, 215, 419, 165, 359, 38, 105...
## $ `6/4/20` <dbl> 0, 241, 293, 177, 76, 63, 217, 421, 167, 360, 38, 107...
## $ `6/5/20` <dbl> 0, 248, 296, 183, 76, 64, 219, 431, 169, 363, 38, 108...
## $ `6/6/20` <dbl> 0, 259, 304, 190, 77, 70, 225, 442, 174, 373, 40, 108...
## $ `6/7/20` <dbl> 0, 265, 313, 193, 77, 72, 232, 449, 176, 378, 42, 110...
## $ `6/8/20` <dbl> 0, 272, 320, 197, 79, 73, 238, 455, 178, 383, 42, 111...
## $ `6/9/20` <dbl> 0, 282, 325, 199, 85, 75, 243, 464, 180, 391, 42, 117...
## $ `6/10/20` <dbl> 0, 295, 331, 208, 89, 79, 248, 471, 182, 401, 42, 118...
## $ `6/11/20` <dbl> 0, 312, 343, 214, 93, 87, 253, 484, 184, 417, 42, 121...
## $ `6/12/20` <dbl> 0, 323, 353, 221, 97, 95, 258, 499, 188, 427, 46, 122...
## $ `6/13/20` <dbl> 0, 331, 361, 226, 100, 102, 276, 517, 190, 438, 47, 1...
## $ `6/14/20` <dbl> 0, 357, 364, 234, 104, 110, 302, 536, 195, 453, 51, 1...
## $ `6/15/20` <dbl> 0, 368, 383, 238, 111, 116, 307, 544, 204, 475, 53, 1...
## $ `6/16/20` <dbl> 0, 373, 389, 245, 116, 121, 310, 551, 206, 485, 53, 1...
## $ `6/17/20` <dbl> 0, 375, 392, 251, 118, 123, 313, 554, 208, 486, 53, 1...
## $ `6/18/20` <dbl> 0, 400, 401, 263, 121, 130, 320, 566, 210, 501, 55, 1...
## $ `6/19/20` <dbl> 0, 411, 413, 266, 126, 139, 320, 569, 210, 507, 58, 1...
## $ `6/20/20` <dbl> 0, 431, 420, 272, 126, 143, 327, 572, 211, 516, 58, 1...
## $ `6/21/20` <dbl> 0, 434, 430, 272, 127, 149, 327, 576, 213, 521, 58, 1...
## $ `6/22/20` <dbl> 0, 442, 437, 277, 129, 153, 328, 578, 215, 528, 58, 1...
## $ `6/23/20` <dbl> 0, 453, 450, 280, 135, 159, 329, 581, 216, 534, 58, 1...
## $ `6/24/20` <dbl> 0, 469, 464, 288, 141, 168, 336, 584, 220, 543, 58, 1...
## $ `6/25/20` <dbl> 0, 479, 477, 305, 149, 176, 351, 588, 233, 549, 64, 1...
## $ `6/26/20` <dbl> 0, 488, 515, 312, 153, 184, 351, 594, 236, 559, 68, 1...
## $ `6/27/20` <dbl> 0, 498, 555, 317, 161, 188, 358, 600, 245, 561, 69, 2...
## $ `6/28/20` <dbl> 0, 503, 575, 317, 162, 189, 358, 602, 245, 561, 70, 2...
## $ `6/29/20` <dbl> 0, 527, 643, 322, 165, 199, 365, 605, 269, 585, 73, 2...
## $ `6/30/20` <dbl> 0, 537, 680, 325, 170, 208, 365, 607, 276, 590, 74, 2...
## $ `7/1/20` <dbl> 0, 553, 703, 326, 174, 218, 367, 607, 278, 595, 77, 2...
## $ `7/2/20` <dbl> 0, 561, 751, 335, 179, 222, 369, 610, 288, 611, 82, 2...
## $ `7/3/20` <dbl> 0, 568, 845, 348, 189, 230, 372, 625, 330, 625, 88, 2...
## $ `7/4/20` <dbl> 0, 591, 863, 350, 190, 234, 373, 626, 340, 637, 88, 2...
## $ `7/5/20` <dbl> 0, 615, 881, 352, 193, 239, 373, 634, 362, 642, 100, ...
## $ `7/6/20` <dbl> 0, 618, 911, 356, 197, 247, 373, 634, 384, 655, 105, ...
## $ `7/7/20` <dbl> 0, 644, 997, 360, 199, 255, 373, 634, 395, 656, 106, ...
## $ `7/8/20` <dbl> 0, 651, 1056, 366, 201, 262, 374, 639, 411, 660, 114,...
## $ `7/9/20` <dbl> 0, 661, 1131, 371, 211, 282, 375, 646, 445, 672, 115,...
## $ `7/10/20` <dbl> 0, 670, 1187, 381, 218, 292, 381, 648, 465, 679, 118,...
## $ `7/11/20` <dbl> 0, 684, 1224, 398, 224, 307, 382, 654, 500, 690, 128,...
## $ `7/12/20` <dbl> 0, 706, 1294, 403, 228, 331, 383, 655, 526, 693, 129,...
## $ `7/13/20` <dbl> 0, 728, 1359, 413, 231, 350, 383, 660, 566, 702, 136,...
## $ `7/14/20` <dbl> 0, 746, 1414, 428, 236, 366, 385, 661, 589, 712, 140,...
## $ `7/15/20` <dbl> 0, 756, 1518, 441, 242, 389, 386, 664, 655, 718, 145,...
## $ `7/16/20` <dbl> 0, 780, 1599, 459, 247, 424, 389, 669, 675, 731, 152,...
## $ `7/17/20` <dbl> 0, 789, 1689, 463, 255, 440, 393, 672, 720, 742, 157,...
## $ `7/18/20` <dbl> 0, 827, 1819, 483, 264, 458, 397, 678, 741, 756, 165,...
## $ `7/19/20` <dbl> 0, 842, 1937, 495, 269, 482, 398, 686, 785, 762, 173,...
## $ `7/20/20` <dbl> 0, 857, 2013, 503, 279, 507, 400, 689, 832, 767, 179,...
## $ `7/21/20` <dbl> 0, 865, 2102, 514, 283, 524, 401, 695, 869, 774, 182,...
## $ `7/22/20` <dbl> 0, 886, 2196, 518, 287, 547, 407, 701, 891, 782, 184,...
## $ `7/23/20` <dbl> 0, 905, 2461, 534, 289, 585, 408, 706, 934, 789, 193,...
## $ `7/24/20` <dbl> 0, 921, 2513, 539, 303, 615, 411, 711, 999, 797, 205,...
## $ `7/25/20` <dbl> 0, 932, 2662, 552, 318, 637, 414, 720, 1062, 810, 207...
## $ `7/26/20` <dbl> 0, 942, 2708, 562, 324, 646, 415, 724, 1113, 821, 209...
## $ `7/27/20` <dbl> 0, 965, 2770, 569, 334, 669, 416, 730, 1194, 825, 220...
## $ `7/28/20` <dbl> 0, 974, 2835, 575, 337, 675, 429, 734, 1243, 836, 221...
## $ `7/29/20` <dbl> 0, 974, 2835, 575, 338, 675, 429, 734, 1244, 836, 221...
## $ `7/30/20` <dbl> 0, 1002, 3028, 585, 352, 731, 435, 747, 1336, 848, 23...
## $ `7/31/20` <dbl> 0, 1015, 3101, 598, 363, 767, 437, 753, 1450, 859, 23...
## $ `8/1/20` <dbl> 0, 1030, 3142, 602, 368, 792, 443, 757, 1480, 861, 24...
## $ `8/2/20` <dbl> 0, 1052, 3223, 610, 372, 813, 445, 765, 1580, 868, 25...
## $ `8/3/20` <dbl> 0, 1066, 3265, 612, 382, 830, 446, 766, 1612, 875, 26...
## $ `8/4/20` <dbl> 0, 1073, 3320, 614, 389, 836, 449, 766, 1646, 882, 26...
## $ `8/5/20` <dbl> 0, 1073, 3380, 615, 392, 839, 452, 769, 1683, 886, 27...
## $ `8/6/20` <dbl> 0, 1096, 3438, 619, 421, 874, 458, 771, 1741, 893, 28...
## $ `8/7/20` <dbl> 0, 1113, 3504, 624, 424, 909, 462, 774, 1777, 899, 29...
## $ `8/8/20` <dbl> 0, 1134, 3564, 628, 434, 923, 471, 773, 1836, 904, 29...
## $ `8/9/20` <dbl> 0, 1215, 3606, 630, 446, 934, 472, 779, 1860, 906, 30...
## $ `8/10/20` <dbl> 0, 1215, 3714, 631, 450, 947, 474, 782, 1883, 909, 30...
## $ `8/11/20` <dbl> 0, 1215, 3736, 643, 455, 958, 489, 785, 1914, 916, 30...
## $ `8/12/20` <dbl> 0, 1241, 3776, 646, 464, 967, 500, 788, 1935, 918, 31...
## $ `8/13/20` <dbl> 0, 1250, 3813, 651, 469, 977, 501, 790, 1959, 919, 32...
## $ `8/14/20` <dbl> 0, 1252, 3860, 656, 477, 989, 502, 796, 1975, 922, 32...
## $ `8/15/20` <dbl> 0, 1262, 3909, 663, 483, 996, 503, 807, 2019, 925, 33...
## $ `8/16/20` <dbl> 0, 1273, 3948, 671, 483, 1005, 504, 811, 2037, 927, 3...
## $ `8/17/20` <dbl> 0, 1274, 3960, 672, 488, 1008, 504, 814, 2055, 928, 3...
## $ `8/18/20` <dbl> 0, 1291, 3977, 674, 490, 1034, 512, 814, 2107, 937, 3...
## $ `8/19/20` <dbl> 0, 1293, 4002, 683, 503, 1049, 530, 814, 2159, 941, 3...
## $ `8/20/20` <dbl> 0, 1293, 4035, 690, 507, 1077, 534, 814, 2214, 949, 3...
## $ `8/21/20` <dbl> 0, 1293, 4054, 690, 509, 1083, 534, 814, 2228, 952, 3...
## $ `8/22/20` <dbl> 0, 1322, 4115, 699, 516, 1096, 536, 822, 2276, 957, 3...
## $ `8/23/20` <dbl> 0, 1324, 4147, 702, 523, 1099, 536, 824, 2286, 958, 3...
## $ `8/24/20` <dbl> 0, 1351, 4167, 720, 526, 1135, 536, 825, 2327, 971, 3...
## $ `8/25/20` <dbl> 0, 1355, 4190, 724, 527, 1160, 536, 826, 2345, 973, 3...
## $ `8/26/20` <dbl> 0, 1366, 4265, 732, 530, 1195, 537, 833, 2400, 983, 3...
## $ `8/27/20` <dbl> 0, 1377, 4311, 739, 533, 1213, 538, 839, 2413, 1011, ...
## $ `8/28/20` <dbl> 0, 1389, 4347, 745, 535, 1219, 541, 840, 2443, 1017, ...
## $ `8/29/20` <dbl> 0, 1400, 4424, 753, 540, 1248, 546, 855, 2499, 1024, ...
## $ `8/30/20` <dbl> 0, 1438, 4525, 757, 550, 1277, 550, 864, 2533, 1027, ...
## $ `8/31/20` <dbl> 0, 1442, 4545, 757, 554, 1287, 551, 866, 2567, 1033, ...
## $ `9/1/20` <dbl> 0, 1453, 4568, 764, 558, 1303, 559, 871, 2619, 1041, ...
## Observations: 715,680
## Variables: 6
## $ countyFIPS <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ countyName <chr> "Statewide Unallocated", "Statewide Unallocated", "State...
## $ state <chr> "AL", "AL", "AL", "AL", "AL", "AL", "AL", "AL", "AL", "A...
## $ stateFIPS <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,...
## $ date <date> 2020-01-22, 2020-01-23, 2020-01-24, 2020-01-25, 2020-01...
## $ cumCases <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...## Parsed with column specification:
## cols(
## .default = col_double(),
## `County Name` = col_character(),
## State = col_character()
## )
## See spec(...) for full column specifications.## Observations: 3,195
## Variables: 228
## $ countyFIPS <dbl> 0, 1001, 1003, 1005, 1007, 1009, 1011, 1013, 1015, 10...
## $ `County Name` <chr> "Statewide Unallocated", "Autauga County", "Baldwin C...
## $ State <chr> "AL", "AL", "AL", "AL", "AL", "AL", "AL", "AL", "AL",...
## $ stateFIPS <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,...
## $ `1/22/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `1/23/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `1/24/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `1/25/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `1/26/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `1/27/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `1/28/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `1/29/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `1/30/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `1/31/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `2/1/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `2/2/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `2/3/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `2/4/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `2/5/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `2/6/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `2/7/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `2/8/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `2/9/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `2/10/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `2/11/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `2/12/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `2/13/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `2/14/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `2/15/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `2/16/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `2/17/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `2/18/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `2/19/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `2/20/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `2/21/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `2/22/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `2/23/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `2/24/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `2/25/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `2/26/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `2/27/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `2/28/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `2/29/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `3/1/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `3/2/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `3/3/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `3/4/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `3/5/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `3/6/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `3/7/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `3/8/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `3/9/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `3/10/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `3/11/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `3/12/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `3/13/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `3/14/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `3/15/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `3/16/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `3/17/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `3/18/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `3/19/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `3/20/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `3/21/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `3/22/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `3/23/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `3/24/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `3/25/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `3/26/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `3/27/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `3/28/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `3/29/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `3/30/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `3/31/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `4/1/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `4/2/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `4/3/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `4/4/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `4/5/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `4/6/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `4/7/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `4/8/20` <dbl> 0, 1, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `4/9/20` <dbl> 0, 1, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `4/10/20` <dbl> 0, 1, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 1,...
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## $ `4/15/20` <dbl> 0, 1, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 1,...
## $ `4/16/20` <dbl> 0, 1, 0, 0, 0, 0, 0, 0, 0, 9, 0, 0, 0, 0, 0, 0, 0, 1,...
## $ `4/17/20` <dbl> 0, 1, 0, 0, 0, 0, 0, 0, 1, 11, 0, 0, 0, 0, 0, 0, 0, 1...
## $ `4/18/20` <dbl> 0, 1, 1, 0, 0, 0, 0, 0, 2, 11, 0, 0, 0, 0, 0, 0, 0, 1...
## $ `4/19/20` <dbl> 0, 1, 1, 0, 0, 0, 0, 0, 2, 11, 0, 0, 0, 0, 0, 0, 0, 1...
## $ `4/20/20` <dbl> 0, 1, 1, 0, 0, 0, 0, 0, 2, 11, 0, 0, 0, 0, 0, 0, 0, 1...
## $ `4/21/20` <dbl> 0, 1, 2, 0, 0, 0, 0, 0, 3, 13, 0, 0, 0, 1, 0, 0, 0, 1...
## $ `4/22/20` <dbl> 0, 1, 2, 0, 0, 0, 0, 0, 3, 16, 0, 0, 0, 1, 0, 1, 0, 1...
## $ `4/23/20` <dbl> 0, 2, 2, 0, 0, 0, 0, 0, 3, 16, 0, 1, 0, 1, 1, 1, 0, 1...
## $ `4/24/20` <dbl> 0, 2, 2, 0, 0, 0, 0, 0, 3, 17, 0, 1, 0, 1, 1, 1, 0, 2...
## $ `4/25/20` <dbl> 0, 2, 2, 0, 0, 0, 0, 0, 3, 18, 0, 1, 0, 1, 1, 1, 0, 2...
## $ `4/26/20` <dbl> 0, 2, 2, 0, 0, 0, 0, 1, 3, 18, 0, 1, 0, 1, 1, 1, 0, 2...
## $ `4/27/20` <dbl> 0, 3, 2, 0, 0, 0, 0, 1, 3, 18, 0, 1, 0, 1, 1, 1, 0, 2...
## $ `4/28/20` <dbl> 0, 3, 2, 0, 0, 0, 0, 1, 3, 19, 0, 1, 0, 1, 1, 1, 0, 2...
## $ `4/29/20` <dbl> 0, 3, 2, 1, 0, 0, 0, 1, 3, 21, 0, 1, 0, 1, 1, 1, 0, 2...
## $ `4/30/20` <dbl> 0, 3, 3, 1, 0, 0, 0, 1, 3, 21, 0, 1, 0, 1, 1, 1, 0, 2...
## $ `5/1/20` <dbl> 0, 3, 4, 1, 0, 0, 0, 1, 3, 21, 0, 1, 0, 1, 1, 1, 0, 2...
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## $ `5/3/20` <dbl> 0, 3, 4, 1, 0, 0, 0, 1, 3, 21, 0, 1, 0, 1, 1, 1, 0, 2...
## $ `5/4/20` <dbl> 0, 3, 4, 1, 0, 0, 0, 1, 3, 21, 0, 1, 0, 1, 1, 1, 0, 2...
## $ `5/5/20` <dbl> 0, 3, 5, 1, 0, 0, 0, 2, 3, 21, 0, 1, 0, 1, 1, 1, 0, 2...
## $ `5/6/20` <dbl> 0, 3, 5, 1, 0, 0, 1, 2, 3, 21, 0, 1, 0, 1, 1, 1, 0, 2...
## $ `5/7/20` <dbl> 0, 3, 5, 1, 0, 0, 1, 2, 3, 21, 0, 1, 0, 1, 1, 1, 0, 2...
## $ `5/8/20` <dbl> 0, 3, 5, 1, 1, 0, 1, 3, 3, 21, 0, 1, 0, 1, 1, 1, 0, 2...
## $ `5/9/20` <dbl> 0, 3, 5, 1, 1, 0, 1, 6, 3, 21, 0, 1, 0, 1, 1, 1, 0, 2...
## $ `5/10/20` <dbl> 0, 3, 5, 1, 1, 0, 1, 6, 3, 21, 0, 1, 0, 1, 1, 1, 0, 2...
## $ `5/11/20` <dbl> 0, 3, 6, 1, 1, 0, 1, 6, 3, 21, 0, 1, 0, 1, 1, 1, 0, 2...
## $ `5/12/20` <dbl> 0, 3, 7, 1, 1, 0, 1, 6, 3, 21, 0, 1, 1, 1, 1, 1, 1, 2...
## $ `5/13/20` <dbl> 0, 3, 7, 1, 1, 0, 1, 6, 3, 22, 0, 1, 2, 1, 1, 1, 1, 2...
## $ `5/14/20` <dbl> 0, 3, 8, 1, 1, 0, 1, 8, 3, 22, 0, 1, 3, 2, 2, 1, 1, 2...
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## $ `5/17/20` <dbl> 0, 3, 8, 1, 1, 1, 1, 9, 3, 22, 0, 1, 3, 2, 2, 1, 1, 2...
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## $ `5/19/20` <dbl> 0, 3, 8, 1, 1, 1, 1, 10, 3, 22, 0, 1, 3, 2, 2, 1, 1, ...
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## $ `5/21/20` <dbl> 0, 3, 8, 1, 1, 1, 1, 11, 3, 23, 0, 1, 3, 2, 2, 1, 1, ...
## $ `5/22/20` <dbl> 0, 3, 9, 1, 1, 1, 1, 11, 3, 23, 2, 1, 4, 2, 2, 1, 1, ...
## $ `5/23/20` <dbl> 0, 3, 9, 1, 1, 1, 1, 11, 3, 23, 2, 1, 4, 2, 2, 1, 1, ...
## $ `5/24/20` <dbl> 0, 3, 9, 1, 1, 1, 1, 12, 3, 23, 2, 1, 4, 2, 2, 1, 1, ...
## $ `5/25/20` <dbl> 0, 3, 9, 1, 1, 1, 3, 12, 3, 24, 2, 1, 4, 2, 2, 1, 1, ...
## $ `5/26/20` <dbl> 0, 3, 9, 1, 1, 1, 3, 13, 3, 24, 2, 1, 7, 2, 2, 1, 1, ...
## $ `5/27/20` <dbl> 0, 3, 9, 1, 1, 1, 3, 13, 3, 24, 2, 1, 7, 2, 2, 1, 1, ...
## $ `5/28/20` <dbl> 0, 3, 9, 1, 1, 1, 4, 15, 3, 24, 2, 1, 8, 2, 2, 1, 1, ...
## $ `5/29/20` <dbl> 0, 3, 9, 1, 1, 1, 4, 16, 3, 24, 3, 1, 8, 2, 2, 1, 1, ...
## $ `5/30/20` <dbl> 0, 4, 9, 1, 1, 1, 4, 17, 3, 25, 3, 1, 8, 2, 2, 1, 1, ...
## $ `5/31/20` <dbl> 0, 4, 9, 1, 1, 1, 5, 18, 3, 25, 3, 1, 8, 2, 2, 1, 1, ...
## $ `6/1/20` <dbl> 0, 5, 9, 1, 1, 1, 6, 18, 3, 25, 3, 1, 10, 2, 2, 1, 1,...
## $ `6/2/20` <dbl> 0, 5, 9, 1, 1, 1, 6, 18, 3, 26, 3, 1, 10, 2, 2, 1, 1,...
## $ `6/3/20` <dbl> 0, 5, 9, 1, 1, 1, 6, 18, 3, 26, 3, 1, 10, 2, 2, 1, 1,...
## $ `6/4/20` <dbl> 0, 5, 9, 1, 1, 1, 6, 18, 3, 26, 3, 1, 10, 2, 2, 1, 1,...
## $ `6/5/20` <dbl> 0, 5, 9, 1, 1, 1, 7, 21, 3, 26, 3, 1, 10, 2, 2, 1, 1,...
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## $ `6/10/20` <dbl> 0, 6, 9, 1, 1, 1, 8, 24, 3, 26, 4, 2, 11, 3, 2, 1, 1,...
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## $ `6/12/20` <dbl> 0, 6, 9, 1, 1, 1, 8, 25, 3, 26, 5, 2, 11, 3, 2, 1, 1,...
## $ `6/13/20` <dbl> 0, 6, 9, 1, 1, 1, 8, 25, 3, 26, 5, 2, 11, 3, 2, 1, 1,...
## $ `6/14/20` <dbl> 0, 6, 9, 1, 1, 1, 8, 25, 3, 26, 5, 2, 11, 3, 2, 1, 1,...
## $ `6/15/20` <dbl> 0, 6, 9, 1, 1, 1, 9, 25, 3, 26, 5, 2, 11, 3, 2, 1, 1,...
## $ `6/16/20` <dbl> 0, 7, 9, 1, 1, 1, 9, 25, 4, 26, 5, 2, 11, 3, 2, 1, 1,...
## $ `6/17/20` <dbl> 0, 7, 9, 1, 1, 1, 9, 25, 4, 26, 5, 2, 11, 3, 2, 1, 1,...
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## $ `6/19/20` <dbl> 0, 8, 9, 1, 1, 1, 10, 26, 4, 27, 6, 3, 11, 4, 2, 1, 1...
## $ `6/20/20` <dbl> 0, 9, 9, 1, 1, 1, 10, 26, 4, 27, 6, 3, 12, 4, 2, 1, 1...
## $ `6/21/20` <dbl> 0, 9, 9, 1, 1, 1, 10, 26, 4, 27, 6, 3, 12, 4, 2, 1, 1...
## $ `6/22/20` <dbl> 0, 9, 9, 1, 1, 1, 10, 26, 4, 27, 6, 3, 12, 4, 2, 1, 1...
## $ `6/23/20` <dbl> 0, 9, 9, 1, 1, 1, 10, 27, 5, 27, 7, 3, 12, 4, 2, 1, 1...
## $ `6/24/20` <dbl> 0, 11, 9, 1, 1, 1, 10, 27, 5, 27, 7, 3, 12, 5, 2, 1, ...
## $ `6/25/20` <dbl> 0, 11, 9, 1, 1, 1, 10, 27, 5, 27, 7, 3, 12, 5, 2, 1, ...
## $ `6/26/20` <dbl> 0, 11, 9, 1, 1, 1, 10, 27, 5, 27, 7, 3, 12, 5, 2, 1, ...
## $ `6/27/20` <dbl> 0, 12, 10, 1, 1, 1, 10, 27, 5, 27, 7, 3, 12, 5, 2, 1,...
## $ `6/28/20` <dbl> 0, 12, 10, 1, 1, 1, 10, 27, 5, 27, 7, 3, 12, 5, 2, 1,...
## $ `6/29/20` <dbl> 0, 12, 10, 1, 1, 1, 10, 27, 5, 27, 7, 3, 12, 5, 2, 1,...
## $ `6/30/20` <dbl> 0, 12, 10, 1, 1, 1, 10, 27, 5, 27, 7, 3, 12, 5, 2, 1,...
## $ `7/1/20` <dbl> 0, 12, 10, 1, 1, 1, 10, 27, 5, 27, 7, 3, 12, 5, 2, 1,...
## $ `7/2/20` <dbl> 0, 13, 10, 1, 1, 1, 10, 27, 5, 27, 7, 3, 12, 6, 2, 1,...
## $ `7/3/20` <dbl> 0, 13, 10, 2, 1, 1, 10, 28, 5, 27, 7, 3, 12, 6, 2, 1,...
## $ `7/4/20` <dbl> 0, 13, 10, 2, 1, 1, 11, 28, 5, 27, 7, 3, 12, 6, 2, 1,...
## $ `7/5/20` <dbl> 0, 13, 10, 2, 1, 1, 11, 28, 5, 27, 7, 3, 12, 6, 2, 1,...
## $ `7/6/20` <dbl> 0, 13, 10, 2, 1, 1, 11, 28, 5, 27, 7, 3, 12, 6, 2, 1,...
## $ `7/7/20` <dbl> 0, 13, 10, 2, 1, 1, 11, 28, 5, 27, 7, 3, 12, 6, 2, 1,...
## $ `7/8/20` <dbl> 0, 13, 10, 2, 1, 1, 11, 28, 5, 27, 7, 3, 12, 6, 2, 1,...
## $ `7/9/20` <dbl> 0, 14, 11, 2, 1, 1, 11, 28, 5, 27, 7, 3, 12, 6, 2, 1,...
## $ `7/10/20` <dbl> 0, 15, 12, 2, 1, 1, 11, 29, 5, 27, 7, 3, 12, 6, 2, 1,...
## $ `7/11/20` <dbl> 0, 15, 12, 2, 1, 1, 11, 29, 5, 27, 7, 3, 12, 6, 2, 1,...
## $ `7/12/20` <dbl> 0, 16, 12, 2, 1, 1, 11, 29, 5, 30, 7, 3, 12, 6, 2, 1,...
## $ `7/13/20` <dbl> 0, 16, 12, 2, 1, 1, 11, 29, 5, 30, 7, 3, 12, 6, 2, 1,...
## $ `7/14/20` <dbl> 0, 18, 12, 3, 2, 1, 11, 31, 6, 32, 7, 4, 12, 6, 2, 1,...
## $ `7/15/20` <dbl> 0, 19, 13, 3, 2, 1, 11, 31, 6, 32, 7, 4, 12, 6, 2, 1,...
## $ `7/16/20` <dbl> 0, 20, 14, 3, 2, 1, 11, 32, 6, 32, 7, 4, 12, 6, 2, 1,...
## $ `7/17/20` <dbl> 0, 21, 14, 3, 2, 1, 11, 33, 6, 32, 7, 4, 12, 6, 2, 1,...
## $ `7/18/20` <dbl> 0, 21, 15, 3, 2, 1, 11, 33, 6, 33, 7, 4, 12, 7, 2, 1,...
## $ `7/19/20` <dbl> 0, 21, 15, 3, 2, 1, 11, 33, 6, 33, 7, 4, 12, 7, 2, 1,...
## $ `7/20/20` <dbl> 0, 21, 15, 4, 2, 1, 11, 33, 6, 33, 7, 4, 12, 7, 2, 1,...
## $ `7/21/20` <dbl> 0, 21, 16, 4, 2, 1, 11, 34, 6, 33, 7, 4, 12, 7, 2, 1,...
## $ `7/22/20` <dbl> 0, 21, 16, 4, 2, 1, 12, 34, 6, 34, 7, 5, 12, 8, 2, 1,...
## $ `7/23/20` <dbl> 0, 21, 17, 4, 2, 1, 12, 35, 6, 34, 7, 5, 12, 9, 2, 1,...
## $ `7/24/20` <dbl> 0, 21, 18, 4, 2, 1, 12, 35, 6, 37, 8, 5, 12, 9, 3, 1,...
## $ `7/25/20` <dbl> 0, 21, 18, 4, 2, 1, 12, 35, 6, 37, 8, 5, 12, 9, 3, 1,...
## $ `7/26/20` <dbl> 0, 21, 18, 4, 2, 1, 12, 35, 6, 37, 8, 5, 12, 9, 3, 1,...
## $ `7/27/20` <dbl> 0, 21, 18, 4, 2, 1, 12, 36, 6, 38, 8, 6, 12, 9, 4, 1,...
## $ `7/28/20` <dbl> 0, 21, 18, 4, 2, 1, 12, 36, 6, 38, 8, 6, 12, 9, 4, 1,...
## $ `7/29/20` <dbl> 0, 21, 21, 4, 2, 3, 12, 36, 6, 38, 8, 6, 12, 9, 4, 1,...
## $ `7/30/20` <dbl> 0, 21, 21, 5, 2, 3, 12, 36, 8, 38, 8, 6, 12, 9, 5, 1,...
## $ `7/31/20` <dbl> 0, 21, 22, 5, 2, 3, 12, 36, 9, 38, 8, 6, 12, 9, 5, 1,...
## $ `8/1/20` <dbl> 0, 21, 22, 5, 2, 3, 12, 36, 9, 38, 8, 6, 12, 9, 5, 1,...
## $ `8/2/20` <dbl> 0, 21, 23, 5, 3, 3, 12, 36, 9, 38, 8, 7, 12, 9, 5, 1,...
## $ `8/3/20` <dbl> 0, 21, 24, 5, 3, 3, 12, 36, 9, 38, 8, 7, 12, 9, 5, 1,...
## $ `8/4/20` <dbl> 0, 21, 24, 5, 3, 3, 12, 36, 12, 38, 8, 7, 12, 9, 5, 1...
## $ `8/5/20` <dbl> 0, 22, 24, 5, 4, 3, 12, 36, 13, 38, 8, 7, 12, 9, 5, 1...
## $ `8/6/20` <dbl> 0, 22, 25, 5, 4, 3, 12, 36, 13, 38, 8, 7, 12, 9, 5, 1...
## $ `8/7/20` <dbl> 0, 22, 25, 5, 4, 3, 12, 36, 13, 38, 8, 8, 12, 9, 5, 1...
## $ `8/8/20` <dbl> 0, 22, 26, 5, 5, 4, 12, 37, 13, 38, 8, 8, 12, 9, 5, 1...
## $ `8/9/20` <dbl> 0, 22, 27, 5, 5, 4, 12, 37, 14, 38, 8, 8, 12, 9, 5, 1...
## $ `8/10/20` <dbl> 0, 22, 28, 5, 5, 4, 12, 37, 17, 38, 9, 9, 12, 10, 5, ...
## $ `8/11/20` <dbl> 0, 23, 32, 6, 6, 5, 12, 37, 19, 38, 9, 12, 12, 10, 5,...
## $ `8/12/20` <dbl> 0, 23, 32, 6, 6, 5, 12, 37, 20, 38, 9, 12, 12, 10, 5,...
## $ `8/13/20` <dbl> 0, 23, 32, 6, 6, 5, 12, 37, 20, 38, 9, 12, 12, 10, 5,...
## $ `8/14/20` <dbl> 0, 23, 32, 6, 6, 5, 12, 37, 20, 38, 9, 12, 12, 10, 5,...
## $ `8/15/20` <dbl> 0, 23, 32, 6, 6, 5, 12, 37, 20, 38, 9, 12, 12, 10, 5,...
## $ `8/16/20` <dbl> 0, 23, 32, 6, 6, 5, 12, 37, 20, 38, 9, 12, 12, 10, 5,...
## $ `8/17/20` <dbl> 0, 23, 32, 6, 6, 5, 14, 37, 23, 38, 9, 12, 12, 10, 5,...
## $ `8/18/20` <dbl> 0, 23, 33, 6, 6, 5, 14, 37, 25, 38, 9, 12, 12, 10, 5,...
## $ `8/19/20` <dbl> 0, 23, 33, 7, 6, 5, 14, 37, 25, 38, 9, 12, 12, 10, 5,...
## $ `8/20/20` <dbl> 0, 23, 34, 7, 6, 5, 14, 37, 25, 38, 10, 12, 12, 10, 5...
## $ `8/21/20` <dbl> 0, 23, 35, 7, 6, 6, 14, 37, 25, 38, 10, 13, 12, 10, 5...
## $ `8/22/20` <dbl> 0, 23, 35, 7, 6, 6, 14, 37, 25, 38, 10, 13, 12, 10, 6...
## $ `8/23/20` <dbl> 0, 23, 35, 7, 6, 6, 14, 37, 25, 38, 10, 13, 12, 10, 6...
## $ `8/24/20` <dbl> 0, 23, 35, 7, 6, 6, 14, 37, 27, 38, 10, 13, 12, 11, 6...
## $ `8/25/20` <dbl> 0, 23, 35, 7, 6, 6, 14, 37, 28, 39, 10, 13, 12, 11, 6...
## $ `8/26/20` <dbl> 0, 23, 36, 7, 6, 7, 14, 37, 28, 39, 10, 13, 12, 12, 6...
## $ `8/27/20` <dbl> 0, 23, 37, 7, 6, 7, 14, 37, 30, 39, 12, 13, 12, 13, 6...
## $ `8/28/20` <dbl> 0, 23, 39, 7, 6, 9, 14, 37, 32, 40, 12, 13, 12, 13, 6...
## $ `8/29/20` <dbl> 0, 23, 40, 7, 7, 9, 14, 37, 35, 40, 12, 13, 12, 14, 6...
## $ `8/30/20` <dbl> 0, 23, 40, 7, 7, 10, 14, 37, 35, 40, 12, 13, 12, 14, ...
## $ `8/31/20` <dbl> 0, 23, 42, 7, 8, 11, 14, 37, 36, 40, 12, 13, 12, 14, ...
## $ `9/1/20` <dbl> 0, 24, 42, 7, 8, 11, 14, 37, 38, 40, 12, 13, 12, 14, ...
## Observations: 715,680
## Variables: 6
## $ countyFIPS <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ countyName <chr> "Statewide Unallocated", "Statewide Unallocated", "State...
## $ state <chr> "AL", "AL", "AL", "AL", "AL", "AL", "AL", "AL", "AL", "A...
## $ stateFIPS <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,...
## $ date <date> 2020-01-22, 2020-01-23, 2020-01-24, 2020-01-25, 2020-01...
## $ cumDeaths <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...# STEP 3: Explore the cases and deaths by county (can be repeated for other counties)
casesDeathsByCounty(useDate=maxDate_20200903,
inclStates=c("FL", "GA", "SC", "AL", "MS"),
caseData=rawUSAFacts_20200903$cases,
deathData=rawUSAFacts_20200903$deaths,
highCaseAmount=80000,
highDeathAmount=2000
)
# STEP 4: Create county-level clusters (k-means, 5 clusters, minimum county population 25k)
clust_20200903_new <- prepClusterCounties(burdenFile=burden_20200903_new,
maxDate=maxDate_20200903,
minPop=25000,
hierarchical=FALSE,
minShape=3,
ratioDeathvsCase = 5,
ratioTotalvsShape = 0.5,
minDeath=100,
minCase=5000,
nCenters=5,
testCenters=1:25,
iter.max=20,
nstart=10,
seed=2009081450
)## # A tibble: 1 x 6
## cpm_mean_is0 dpm_mean_is0 cpm_mean_lt100 dpm_mean_lt100 cpm_mean_lt5000
## <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 0 0.0352 0 0.266 0.140
## # ... with 1 more variable: dpm_mean_lt5000 <dbl>
##
## Cluster means and counts
## 1 2 3 4 5
## . 501.00 473.00 53.00 199.00 365.00
## totalCases 0.69 0.32 1.23 1.14 0.52
## totalDeaths 1.36 0.36 9.67 4.76 1.86
## cases_3 0.01 0.02 0.08 0.02 0.02
## deaths_3 0.05 0.08 0.12 0.05 0.09
## cases_4 0.04 0.06 0.39 0.14 0.17
## deaths_4 0.35 0.54 2.07 0.85 1.45
## cases_5 0.05 0.08 0.17 0.14 0.17
## deaths_5 0.29 0.34 1.29 0.99 1.72
## cases_6 0.13 0.11 0.10 0.15 0.12
## deaths_6 0.36 0.52 0.62 0.70 0.73
## cases_7 0.39 0.27 0.14 0.32 0.24
## deaths_7 1.25 0.69 0.46 1.09 0.45
## cases_8 0.38 0.32 0.12 0.24 0.25
## deaths_8 2.61 0.58 0.43 1.32 0.51# STEP 5: Extract the clusters from the clustering object
clustVec_county_20200903_new <- clust_20200903_new$objCluster$objCluster$cluster
# STEP 6: Assess the quality of the new clusters
helperACC_county_20200903_new <- helperAssessCountyClusters(clustVec_county_20200903_new,
dfPop=clust_20200903_new$countyFiltered,
dfBurden=clust_20200903_new$countyFiltered,
thruLabel="Sep 3, 2020",
plotsTogether=TRUE,
orderCluster=TRUE
)
##
## Recency is defined as 2020-08-02 through current
##
## Recency is defined as 2020-08-02 through current
# STEP 7: Create a plot of cumulative burden by cluster
helperACC_county_20200903_new %>%
select(cluster, date, pop, cases, deaths) %>%
group_by(cluster, date) %>%
summarize_if(is.numeric, sum, na.rm=TRUE) %>%
arrange(date) %>%
mutate(cpmcum=cumsum(cases)*1000000/pop, dpmcum=cumsum(deaths)*1000000/pop) %>%
ungroup() %>%
select(cluster, date, cases=cpmcum, deaths=dpmcum) %>%
pivot_longer(-c(cluster, date)) %>%
ggplot(aes(x=date, y=value, color=cluster)) +
geom_line(size=1) +
geom_text(data=~filter(., date==max(date)),
aes(x=date+lubridate::days(2), label=round(value)),
size=3,
hjust=0
) +
labs(x="", title="Cumulative burden per million people by segment", y="") +
facet_wrap(~c("cases"="Cases per million", "deaths"="Deaths per million")[name], scales="free_y") +
scale_x_date(date_breaks="1 months", date_labels="%b", expand=expand_scale(c(0, 0.1)))
# STEP 8: Add back clusters not used for analysis (code 999) and associated disease data
clusterStateData_20200903_new <- helperMakeClusterStateData(helperACC_county_20200903_new,
dfBurden=clust_20200903_new$countyDailyPerCapita,
orderCluster=TRUE
)## Joining, by = "fipsCounty"## Joining, by = "fipsCounty"# STEP 9: Run an example state-level summary (can expand to other states)
stateCountySummary(states=c("MN", "ND", "SD", "WI"),
df=changeOrderLabel(clusterStateData_20200903_new, grpVars="fipsCounty"),
keyDate=maxDate_20200903,
showQuadrants=TRUE,
showCumulative=TRUE,
facetCumulativeByState = TRUE,
showAllFactorLevels = TRUE
)
# STEP 10a: Read in updated raw data
caseFile_20200917 <- "./RInputFiles/Coronavirus/covid_confirmed_usafacts_downloaded_20200917.csv"
deathFile_20200917 <- "./RInputFiles/Coronavirus/covid_deaths_usafacts_downloaded_20200917.csv"
maxDate_20200917 <- "2020-09-15"
rawUSAFacts_20200917 <- readPivotUSAFacts(popFile=popFile,
caseFile=caseFile_20200917,
deathFile=deathFile_20200917,
unassignedDate=maxDate_20200917
)## Parsed with column specification:
## cols(
## countyFIPS = col_double(),
## `County Name` = col_character(),
## State = col_character(),
## population = col_double()
## )## Parsed with column specification:
## cols(
## .default = col_double(),
## `County Name` = col_character(),
## State = col_character()
## )## See spec(...) for full column specifications.## Parsed with column specification:
## cols(
## .default = col_double(),
## `County Name` = col_character(),
## State = col_character()
## )## See spec(...) for full column specifications.## Joining, by = c("state", "date", "unassigned")
# STEP 10b: Use existing state-level segments with updated raw data
burden_20200917 <- readUSAFacts(
caseFile=caseFile_20200917,
deathFile=deathFile_20200917,
stateClusters=test_hier5_201001$useClusters
)## Parsed with column specification:
## cols(
## .default = col_double(),
## `County Name` = col_character(),
## State = col_character()
## )## See spec(...) for full column specifications.## Observations: 3,195
## Variables: 242
## $ countyFIPS <dbl> 0, 1001, 1003, 1005, 1007, 1009, 1011, 1013, 1015, 10...
## $ `County Name` <chr> "Statewide Unallocated", "Autauga County", "Baldwin C...
## $ State <chr> "AL", "AL", "AL", "AL", "AL", "AL", "AL", "AL", "AL",...
## $ stateFIPS <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,...
## $ `1/22/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
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## $ `6/17/20` <dbl> 0, 375, 392, 251, 118, 123, 313, 554, 208, 486, 53, 1...
## $ `6/18/20` <dbl> 0, 400, 401, 263, 121, 130, 320, 566, 210, 501, 55, 1...
## $ `6/19/20` <dbl> 0, 411, 413, 266, 126, 139, 320, 569, 210, 507, 58, 1...
## $ `6/20/20` <dbl> 0, 431, 420, 272, 126, 143, 327, 572, 211, 516, 58, 1...
## $ `6/21/20` <dbl> 0, 434, 430, 272, 127, 149, 327, 576, 213, 521, 58, 1...
## $ `6/22/20` <dbl> 0, 442, 437, 277, 129, 153, 328, 578, 215, 528, 58, 1...
## $ `6/23/20` <dbl> 0, 453, 450, 280, 135, 159, 329, 581, 216, 534, 58, 1...
## $ `6/24/20` <dbl> 0, 469, 464, 288, 141, 168, 336, 584, 220, 543, 58, 1...
## $ `6/25/20` <dbl> 0, 479, 477, 305, 149, 176, 351, 588, 233, 549, 64, 1...
## $ `6/26/20` <dbl> 0, 488, 515, 312, 153, 184, 351, 594, 236, 559, 68, 1...
## $ `6/27/20` <dbl> 0, 498, 555, 317, 161, 188, 358, 600, 245, 561, 69, 2...
## $ `6/28/20` <dbl> 0, 503, 575, 317, 162, 189, 358, 602, 245, 561, 70, 2...
## $ `6/29/20` <dbl> 0, 527, 643, 322, 165, 199, 365, 605, 269, 585, 73, 2...
## $ `6/30/20` <dbl> 0, 537, 680, 325, 170, 208, 365, 607, 276, 590, 74, 2...
## $ `7/1/20` <dbl> 0, 553, 703, 326, 174, 218, 367, 607, 278, 595, 77, 2...
## $ `7/2/20` <dbl> 0, 561, 751, 335, 179, 222, 369, 610, 288, 611, 82, 2...
## $ `7/3/20` <dbl> 0, 568, 845, 348, 189, 230, 372, 625, 330, 625, 88, 2...
## $ `7/4/20` <dbl> 0, 591, 863, 350, 190, 234, 373, 626, 340, 637, 88, 2...
## $ `7/5/20` <dbl> 0, 615, 881, 352, 193, 239, 373, 634, 362, 642, 100, ...
## $ `7/6/20` <dbl> 0, 618, 911, 356, 197, 247, 373, 634, 384, 655, 105, ...
## $ `7/7/20` <dbl> 0, 644, 997, 360, 199, 255, 373, 634, 395, 656, 106, ...
## $ `7/8/20` <dbl> 0, 651, 1056, 366, 201, 262, 374, 639, 411, 660, 114,...
## $ `7/9/20` <dbl> 0, 661, 1131, 371, 211, 282, 375, 646, 445, 672, 115,...
## $ `7/10/20` <dbl> 0, 670, 1187, 381, 218, 292, 381, 648, 465, 679, 118,...
## $ `7/11/20` <dbl> 0, 684, 1224, 398, 224, 307, 382, 654, 500, 690, 128,...
## $ `7/12/20` <dbl> 0, 706, 1294, 403, 228, 331, 383, 655, 526, 693, 129,...
## $ `7/13/20` <dbl> 0, 728, 1359, 413, 231, 350, 383, 660, 566, 702, 136,...
## $ `7/14/20` <dbl> 0, 746, 1414, 428, 236, 366, 385, 661, 589, 712, 140,...
## $ `7/15/20` <dbl> 0, 756, 1518, 441, 242, 389, 386, 664, 655, 718, 145,...
## $ `7/16/20` <dbl> 0, 780, 1599, 459, 247, 424, 389, 669, 675, 731, 152,...
## $ `7/17/20` <dbl> 0, 789, 1689, 463, 255, 440, 393, 672, 720, 742, 157,...
## $ `7/18/20` <dbl> 0, 827, 1819, 483, 264, 458, 397, 678, 741, 756, 165,...
## $ `7/19/20` <dbl> 0, 842, 1937, 495, 269, 482, 398, 686, 785, 762, 173,...
## $ `7/20/20` <dbl> 0, 857, 2013, 503, 279, 507, 400, 689, 832, 767, 179,...
## $ `7/21/20` <dbl> 0, 865, 2102, 514, 283, 524, 401, 695, 869, 774, 182,...
## $ `7/22/20` <dbl> 0, 886, 2196, 518, 287, 547, 407, 701, 891, 782, 184,...
## $ `7/23/20` <dbl> 0, 905, 2461, 534, 289, 585, 408, 706, 934, 789, 193,...
## $ `7/24/20` <dbl> 0, 921, 2513, 539, 303, 615, 411, 711, 999, 797, 205,...
## $ `7/25/20` <dbl> 0, 932, 2662, 552, 318, 637, 414, 720, 1062, 810, 207...
## $ `7/26/20` <dbl> 0, 942, 2708, 562, 324, 646, 415, 724, 1113, 821, 209...
## $ `7/27/20` <dbl> 0, 965, 2770, 569, 334, 669, 416, 730, 1194, 825, 220...
## $ `7/28/20` <dbl> 0, 974, 2835, 575, 337, 675, 429, 734, 1243, 836, 221...
## $ `7/29/20` <dbl> 0, 974, 2835, 575, 338, 675, 429, 734, 1244, 836, 221...
## $ `7/30/20` <dbl> 0, 1002, 3028, 585, 352, 731, 435, 747, 1336, 848, 23...
## $ `7/31/20` <dbl> 0, 1015, 3101, 598, 363, 767, 437, 753, 1450, 859, 23...
## $ `8/1/20` <dbl> 0, 1030, 3142, 602, 368, 792, 443, 757, 1480, 861, 24...
## $ `8/2/20` <dbl> 0, 1052, 3223, 610, 372, 813, 445, 765, 1580, 868, 25...
## $ `8/3/20` <dbl> 0, 1066, 3265, 612, 382, 830, 446, 766, 1612, 875, 26...
## $ `8/4/20` <dbl> 0, 1073, 3320, 614, 389, 836, 449, 766, 1646, 882, 26...
## $ `8/5/20` <dbl> 0, 1073, 3380, 615, 392, 839, 452, 769, 1683, 886, 27...
## $ `8/6/20` <dbl> 0, 1096, 3438, 619, 421, 874, 458, 771, 1741, 893, 28...
## $ `8/7/20` <dbl> 0, 1113, 3504, 624, 424, 909, 462, 774, 1777, 899, 29...
## $ `8/8/20` <dbl> 0, 1134, 3564, 628, 434, 923, 471, 773, 1836, 904, 29...
## $ `8/9/20` <dbl> 0, 1215, 3606, 630, 446, 934, 472, 779, 1860, 906, 30...
## $ `8/10/20` <dbl> 0, 1215, 3714, 631, 450, 947, 474, 782, 1883, 909, 30...
## $ `8/11/20` <dbl> 0, 1215, 3736, 643, 455, 958, 489, 785, 1914, 916, 30...
## $ `8/12/20` <dbl> 0, 1241, 3776, 646, 464, 967, 500, 788, 1935, 918, 31...
## $ `8/13/20` <dbl> 0, 1250, 3813, 651, 469, 977, 501, 790, 1959, 919, 32...
## $ `8/14/20` <dbl> 0, 1252, 3860, 656, 477, 989, 502, 796, 1975, 922, 32...
## $ `8/15/20` <dbl> 0, 1262, 3909, 663, 483, 996, 503, 807, 2019, 925, 33...
## $ `8/16/20` <dbl> 0, 1273, 3948, 671, 483, 1005, 504, 811, 2037, 927, 3...
## $ `8/17/20` <dbl> 0, 1274, 3960, 672, 488, 1008, 504, 814, 2055, 928, 3...
## $ `8/18/20` <dbl> 0, 1291, 3977, 674, 490, 1034, 512, 814, 2107, 937, 3...
## $ `8/19/20` <dbl> 0, 1293, 4002, 683, 503, 1049, 530, 814, 2159, 941, 3...
## $ `8/20/20` <dbl> 0, 1293, 4035, 690, 507, 1077, 534, 814, 2214, 949, 3...
## $ `8/21/20` <dbl> 0, 1293, 4054, 690, 509, 1083, 534, 814, 2228, 952, 3...
## $ `8/22/20` <dbl> 0, 1322, 4115, 699, 516, 1096, 536, 822, 2276, 957, 3...
## $ `8/23/20` <dbl> 0, 1324, 4147, 702, 523, 1099, 536, 824, 2286, 958, 3...
## $ `8/24/20` <dbl> 0, 1351, 4167, 720, 526, 1135, 536, 825, 2327, 971, 3...
## $ `8/25/20` <dbl> 0, 1355, 4190, 724, 527, 1160, 536, 826, 2345, 973, 3...
## $ `8/26/20` <dbl> 0, 1366, 4265, 732, 530, 1195, 537, 833, 2400, 983, 3...
## $ `8/27/20` <dbl> 0, 1377, 4311, 739, 533, 1213, 538, 839, 2413, 1011, ...
## $ `8/28/20` <dbl> 0, 1389, 4347, 745, 535, 1219, 541, 840, 2443, 1017, ...
## $ `8/29/20` <dbl> 0, 1400, 4424, 753, 540, 1248, 546, 855, 2499, 1024, ...
## $ `8/30/20` <dbl> 0, 1438, 4525, 757, 550, 1277, 550, 864, 2533, 1027, ...
## $ `8/31/20` <dbl> 0, 1442, 4545, 757, 554, 1287, 551, 866, 2567, 1033, ...
## $ `9/1/20` <dbl> 0, 1452, 4568, 764, 558, 1303, 559, 871, 2619, 1041, ...
## $ `9/2/20` <dbl> 0, 1452, 4583, 768, 562, 1308, 561, 872, 2633, 1045, ...
## $ `9/3/20` <dbl> 0, 1466, 4628, 771, 564, 1336, 563, 874, 2678, 1046, ...
## $ `9/4/20` <dbl> 0, 1475, 4654, 776, 570, 1361, 563, 881, 2747, 1054, ...
## $ `9/5/20` <dbl> 0, 1492, 4686, 776, 576, 1376, 566, 886, 2830, 1059, ...
## $ `9/6/20` <dbl> 0, 1498, 4713, 777, 581, 1379, 568, 890, 2842, 1061, ...
## $ `9/7/20` <dbl> 0, 1504, 4730, 778, 583, 1384, 568, 892, 2877, 1063, ...
## $ `9/8/20` <dbl> 0, 1508, 4757, 778, 589, 1390, 568, 892, 2891, 1064, ...
## $ `9/9/20` <dbl> 0, 1522, 4787, 778, 591, 1401, 570, 895, 2907, 1068, ...
## $ `9/10/20` <dbl> 0, 1544, 4833, 785, 594, 1430, 572, 896, 2958, 1076, ...
## $ `9/11/20` <dbl> 0, 1551, 4886, 786, 602, 1441, 573, 896, 2988, 1088, ...
## $ `9/12/20` <dbl> 0, 1565, 4922, 792, 604, 1446, 574, 898, 3047, 1094, ...
## $ `9/13/20` <dbl> 0, 1576, 4959, 794, 607, 1453, 580, 899, 3093, 1094, ...
## $ `9/14/20` <dbl> 0, 1585, 4978, 801, 610, 1464, 580, 900, 3110, 1097, ...
## $ `9/15/20` <dbl> 0, 1601, 4992, 806, 611, 1475, 581, 901, 3127, 1102, ...
## Observations: 760,410
## Variables: 6
## $ countyFIPS <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ countyName <chr> "Statewide Unallocated", "Statewide Unallocated", "State...
## $ state <chr> "AL", "AL", "AL", "AL", "AL", "AL", "AL", "AL", "AL", "A...
## $ stateFIPS <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,...
## $ date <date> 2020-01-22, 2020-01-23, 2020-01-24, 2020-01-25, 2020-01...
## $ cumCases <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...## Parsed with column specification:
## cols(
## .default = col_double(),
## `County Name` = col_character(),
## State = col_character()
## )
## See spec(...) for full column specifications.## Observations: 3,195
## Variables: 242
## $ countyFIPS <dbl> 0, 1001, 1003, 1005, 1007, 1009, 1011, 1013, 1015, 10...
## $ `County Name` <chr> "Statewide Unallocated", "Autauga County", "Baldwin C...
## $ State <chr> "AL", "AL", "AL", "AL", "AL", "AL", "AL", "AL", "AL",...
## $ stateFIPS <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,...
## $ `1/22/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `1/23/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `1/24/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `1/25/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `1/26/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `1/27/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `1/28/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `1/29/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `1/30/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `1/31/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `2/1/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `2/2/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `2/3/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `2/4/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `2/5/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `2/6/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `2/7/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `2/8/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `2/9/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `2/10/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `2/11/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `2/12/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `2/13/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `2/14/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `2/15/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `2/16/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `2/17/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `2/18/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `2/19/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `2/20/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `2/21/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `2/22/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `2/23/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `2/24/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `2/25/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `2/26/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `2/27/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `2/28/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `2/29/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `3/1/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `3/2/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `3/3/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `3/4/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `3/5/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `3/6/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `3/7/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `3/8/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `3/9/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `3/10/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `3/11/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `3/12/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `3/13/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `3/14/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `3/15/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `3/16/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `3/17/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `3/18/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `3/19/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `3/20/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `3/21/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `3/22/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `3/23/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `3/24/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `3/25/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `3/26/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `3/27/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `3/28/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `3/29/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `3/30/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `3/31/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `4/1/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `4/2/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `4/3/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `4/4/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `4/5/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `4/6/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `4/7/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `4/8/20` <dbl> 0, 1, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `4/9/20` <dbl> 0, 1, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `4/10/20` <dbl> 0, 1, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 1,...
## $ `4/11/20` <dbl> 0, 1, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 1,...
## $ `4/12/20` <dbl> 0, 1, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 1,...
## $ `4/13/20` <dbl> 0, 1, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 1,...
## $ `4/14/20` <dbl> 0, 1, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 1,...
## $ `4/15/20` <dbl> 0, 1, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 1,...
## $ `4/16/20` <dbl> 0, 1, 0, 0, 0, 0, 0, 0, 0, 9, 0, 0, 0, 0, 0, 0, 0, 1,...
## $ `4/17/20` <dbl> 0, 1, 0, 0, 0, 0, 0, 0, 1, 11, 0, 0, 0, 0, 0, 0, 0, 1...
## $ `4/18/20` <dbl> 0, 1, 1, 0, 0, 0, 0, 0, 2, 11, 0, 0, 0, 0, 0, 0, 0, 1...
## $ `4/19/20` <dbl> 0, 1, 1, 0, 0, 0, 0, 0, 2, 11, 0, 0, 0, 0, 0, 0, 0, 1...
## $ `4/20/20` <dbl> 0, 1, 1, 0, 0, 0, 0, 0, 2, 11, 0, 0, 0, 0, 0, 0, 0, 1...
## $ `4/21/20` <dbl> 0, 1, 2, 0, 0, 0, 0, 0, 3, 13, 0, 0, 0, 1, 0, 0, 0, 1...
## $ `4/22/20` <dbl> 0, 1, 2, 0, 0, 0, 0, 0, 3, 16, 0, 0, 0, 1, 0, 1, 0, 1...
## $ `4/23/20` <dbl> 0, 2, 2, 0, 0, 0, 0, 0, 3, 16, 0, 1, 0, 1, 1, 1, 0, 1...
## $ `4/24/20` <dbl> 0, 2, 2, 0, 0, 0, 0, 0, 3, 17, 0, 1, 0, 1, 1, 1, 0, 2...
## $ `4/25/20` <dbl> 0, 2, 2, 0, 0, 0, 0, 0, 3, 18, 0, 1, 0, 1, 1, 1, 0, 2...
## $ `4/26/20` <dbl> 0, 2, 2, 0, 0, 0, 0, 1, 3, 18, 0, 1, 0, 1, 1, 1, 0, 2...
## $ `4/27/20` <dbl> 0, 3, 2, 0, 0, 0, 0, 1, 3, 18, 0, 1, 0, 1, 1, 1, 0, 2...
## $ `4/28/20` <dbl> 0, 3, 2, 0, 0, 0, 0, 1, 3, 19, 0, 1, 0, 1, 1, 1, 0, 2...
## $ `4/29/20` <dbl> 0, 3, 2, 1, 0, 0, 0, 1, 3, 21, 0, 1, 0, 1, 1, 1, 0, 2...
## $ `4/30/20` <dbl> 0, 3, 3, 1, 0, 0, 0, 1, 3, 21, 0, 1, 0, 1, 1, 1, 0, 2...
## $ `5/1/20` <dbl> 0, 3, 4, 1, 0, 0, 0, 1, 3, 21, 0, 1, 0, 1, 1, 1, 0, 2...
## $ `5/2/20` <dbl> 0, 3, 4, 1, 0, 0, 0, 1, 3, 21, 0, 1, 0, 1, 1, 1, 0, 2...
## $ `5/3/20` <dbl> 0, 3, 4, 1, 0, 0, 0, 1, 3, 21, 0, 1, 0, 1, 1, 1, 0, 2...
## $ `5/4/20` <dbl> 0, 3, 4, 1, 0, 0, 0, 1, 3, 21, 0, 1, 0, 1, 1, 1, 0, 2...
## $ `5/5/20` <dbl> 0, 3, 5, 1, 0, 0, 0, 2, 3, 21, 0, 1, 0, 1, 1, 1, 0, 2...
## $ `5/6/20` <dbl> 0, 3, 5, 1, 0, 0, 1, 2, 3, 21, 0, 1, 0, 1, 1, 1, 0, 2...
## $ `5/7/20` <dbl> 0, 3, 5, 1, 0, 0, 1, 2, 3, 21, 0, 1, 0, 1, 1, 1, 0, 2...
## $ `5/8/20` <dbl> 0, 3, 5, 1, 1, 0, 1, 3, 3, 21, 0, 1, 0, 1, 1, 1, 0, 2...
## $ `5/9/20` <dbl> 0, 3, 5, 1, 1, 0, 1, 6, 3, 21, 0, 1, 0, 1, 1, 1, 0, 2...
## $ `5/10/20` <dbl> 0, 3, 5, 1, 1, 0, 1, 6, 3, 21, 0, 1, 0, 1, 1, 1, 0, 2...
## $ `5/11/20` <dbl> 0, 3, 6, 1, 1, 0, 1, 6, 3, 21, 0, 1, 0, 1, 1, 1, 0, 2...
## $ `5/12/20` <dbl> 0, 3, 7, 1, 1, 0, 1, 6, 3, 21, 0, 1, 1, 1, 1, 1, 1, 2...
## $ `5/13/20` <dbl> 0, 3, 7, 1, 1, 0, 1, 6, 3, 22, 0, 1, 2, 1, 1, 1, 1, 2...
## $ `5/14/20` <dbl> 0, 3, 8, 1, 1, 0, 1, 8, 3, 22, 0, 1, 3, 2, 2, 1, 1, 2...
## $ `5/15/20` <dbl> 0, 3, 8, 1, 1, 0, 1, 9, 3, 22, 0, 1, 3, 2, 2, 1, 1, 2...
## $ `5/16/20` <dbl> 0, 3, 8, 1, 1, 0, 1, 9, 3, 22, 0, 1, 3, 2, 2, 1, 1, 2...
## $ `5/17/20` <dbl> 0, 3, 8, 1, 1, 1, 1, 9, 3, 22, 0, 1, 3, 2, 2, 1, 1, 2...
## $ `5/18/20` <dbl> 0, 3, 8, 1, 1, 1, 1, 10, 3, 22, 0, 1, 3, 2, 2, 1, 1, ...
## $ `5/19/20` <dbl> 0, 3, 8, 1, 1, 1, 1, 10, 3, 22, 0, 1, 3, 2, 2, 1, 1, ...
## $ `5/20/20` <dbl> 0, 3, 8, 1, 1, 1, 1, 11, 3, 23, 0, 1, 3, 2, 2, 1, 1, ...
## $ `5/21/20` <dbl> 0, 3, 8, 1, 1, 1, 1, 11, 3, 23, 0, 1, 3, 2, 2, 1, 1, ...
## $ `5/22/20` <dbl> 0, 3, 9, 1, 1, 1, 1, 11, 3, 23, 2, 1, 4, 2, 2, 1, 1, ...
## $ `5/23/20` <dbl> 0, 3, 9, 1, 1, 1, 1, 11, 3, 23, 2, 1, 4, 2, 2, 1, 1, ...
## $ `5/24/20` <dbl> 0, 3, 9, 1, 1, 1, 1, 12, 3, 23, 2, 1, 4, 2, 2, 1, 1, ...
## $ `5/25/20` <dbl> 0, 3, 9, 1, 1, 1, 3, 12, 3, 24, 2, 1, 4, 2, 2, 1, 1, ...
## $ `5/26/20` <dbl> 0, 3, 9, 1, 1, 1, 3, 13, 3, 24, 2, 1, 7, 2, 2, 1, 1, ...
## $ `5/27/20` <dbl> 0, 3, 9, 1, 1, 1, 3, 13, 3, 24, 2, 1, 7, 2, 2, 1, 1, ...
## $ `5/28/20` <dbl> 0, 3, 9, 1, 1, 1, 4, 15, 3, 24, 2, 1, 8, 2, 2, 1, 1, ...
## $ `5/29/20` <dbl> 0, 3, 9, 1, 1, 1, 4, 16, 3, 24, 3, 1, 8, 2, 2, 1, 1, ...
## $ `5/30/20` <dbl> 0, 4, 9, 1, 1, 1, 4, 17, 3, 25, 3, 1, 8, 2, 2, 1, 1, ...
## $ `5/31/20` <dbl> 0, 4, 9, 1, 1, 1, 5, 18, 3, 25, 3, 1, 8, 2, 2, 1, 1, ...
## $ `6/1/20` <dbl> 0, 5, 9, 1, 1, 1, 6, 18, 3, 25, 3, 1, 10, 2, 2, 1, 1,...
## $ `6/2/20` <dbl> 0, 5, 9, 1, 1, 1, 6, 18, 3, 26, 3, 1, 10, 2, 2, 1, 1,...
## $ `6/3/20` <dbl> 0, 5, 9, 1, 1, 1, 6, 18, 3, 26, 3, 1, 10, 2, 2, 1, 1,...
## $ `6/4/20` <dbl> 0, 5, 9, 1, 1, 1, 6, 18, 3, 26, 3, 1, 10, 2, 2, 1, 1,...
## $ `6/5/20` <dbl> 0, 5, 9, 1, 1, 1, 7, 21, 3, 26, 3, 1, 10, 2, 2, 1, 1,...
## $ `6/6/20` <dbl> 0, 5, 9, 1, 1, 1, 7, 22, 3, 26, 4, 2, 10, 2, 2, 1, 1,...
## $ `6/7/20` <dbl> 0, 5, 9, 1, 1, 1, 7, 22, 3, 26, 4, 2, 10, 2, 2, 1, 1,...
## $ `6/8/20` <dbl> 0, 5, 9, 1, 1, 1, 8, 24, 3, 26, 4, 2, 10, 3, 2, 1, 1,...
## $ `6/9/20` <dbl> 0, 5, 9, 1, 1, 1, 8, 24, 3, 26, 4, 2, 10, 3, 2, 1, 1,...
## $ `6/10/20` <dbl> 0, 6, 9, 1, 1, 1, 8, 24, 3, 26, 4, 2, 11, 3, 2, 1, 1,...
## $ `6/11/20` <dbl> 0, 6, 9, 1, 1, 1, 8, 25, 3, 26, 4, 2, 11, 3, 2, 1, 1,...
## $ `6/12/20` <dbl> 0, 6, 9, 1, 1, 1, 8, 25, 3, 26, 5, 2, 11, 3, 2, 1, 1,...
## $ `6/13/20` <dbl> 0, 6, 9, 1, 1, 1, 8, 25, 3, 26, 5, 2, 11, 3, 2, 1, 1,...
## $ `6/14/20` <dbl> 0, 6, 9, 1, 1, 1, 8, 25, 3, 26, 5, 2, 11, 3, 2, 1, 1,...
## $ `6/15/20` <dbl> 0, 6, 9, 1, 1, 1, 9, 25, 3, 26, 5, 2, 11, 3, 2, 1, 1,...
## $ `6/16/20` <dbl> 0, 7, 9, 1, 1, 1, 9, 25, 4, 26, 5, 2, 11, 3, 2, 1, 1,...
## $ `6/17/20` <dbl> 0, 7, 9, 1, 1, 1, 9, 25, 4, 26, 5, 2, 11, 3, 2, 1, 1,...
## $ `6/18/20` <dbl> 0, 8, 9, 1, 1, 1, 9, 25, 4, 26, 5, 3, 11, 4, 2, 1, 1,...
## $ `6/19/20` <dbl> 0, 8, 9, 1, 1, 1, 10, 26, 4, 27, 6, 3, 11, 4, 2, 1, 1...
## $ `6/20/20` <dbl> 0, 9, 9, 1, 1, 1, 10, 26, 4, 27, 6, 3, 12, 4, 2, 1, 1...
## $ `6/21/20` <dbl> 0, 9, 9, 1, 1, 1, 10, 26, 4, 27, 6, 3, 12, 4, 2, 1, 1...
## $ `6/22/20` <dbl> 0, 9, 9, 1, 1, 1, 10, 26, 4, 27, 6, 3, 12, 4, 2, 1, 1...
## $ `6/23/20` <dbl> 0, 9, 9, 1, 1, 1, 10, 27, 5, 27, 7, 3, 12, 4, 2, 1, 1...
## $ `6/24/20` <dbl> 0, 11, 9, 1, 1, 1, 10, 27, 5, 27, 7, 3, 12, 5, 2, 1, ...
## $ `6/25/20` <dbl> 0, 11, 9, 1, 1, 1, 10, 27, 5, 27, 7, 3, 12, 5, 2, 1, ...
## $ `6/26/20` <dbl> 0, 11, 9, 1, 1, 1, 10, 27, 5, 27, 7, 3, 12, 5, 2, 1, ...
## $ `6/27/20` <dbl> 0, 12, 10, 1, 1, 1, 10, 27, 5, 27, 7, 3, 12, 5, 2, 1,...
## $ `6/28/20` <dbl> 0, 12, 10, 1, 1, 1, 10, 27, 5, 27, 7, 3, 12, 5, 2, 1,...
## $ `6/29/20` <dbl> 0, 12, 10, 1, 1, 1, 10, 27, 5, 27, 7, 3, 12, 5, 2, 1,...
## $ `6/30/20` <dbl> 0, 12, 10, 1, 1, 1, 10, 27, 5, 27, 7, 3, 12, 5, 2, 1,...
## $ `7/1/20` <dbl> 0, 12, 10, 1, 1, 1, 10, 27, 5, 27, 7, 3, 12, 5, 2, 1,...
## $ `7/2/20` <dbl> 0, 13, 10, 1, 1, 1, 10, 27, 5, 27, 7, 3, 12, 6, 2, 1,...
## $ `7/3/20` <dbl> 0, 13, 10, 2, 1, 1, 10, 28, 5, 27, 7, 3, 12, 6, 2, 1,...
## $ `7/4/20` <dbl> 0, 13, 10, 2, 1, 1, 11, 28, 5, 27, 7, 3, 12, 6, 2, 1,...
## $ `7/5/20` <dbl> 0, 13, 10, 2, 1, 1, 11, 28, 5, 27, 7, 3, 12, 6, 2, 1,...
## $ `7/6/20` <dbl> 0, 13, 10, 2, 1, 1, 11, 28, 5, 27, 7, 3, 12, 6, 2, 1,...
## $ `7/7/20` <dbl> 0, 13, 10, 2, 1, 1, 11, 28, 5, 27, 7, 3, 12, 6, 2, 1,...
## $ `7/8/20` <dbl> 0, 13, 10, 2, 1, 1, 11, 28, 5, 27, 7, 3, 12, 6, 2, 1,...
## $ `7/9/20` <dbl> 0, 14, 11, 2, 1, 1, 11, 28, 5, 27, 7, 3, 12, 6, 2, 1,...
## $ `7/10/20` <dbl> 0, 15, 12, 2, 1, 1, 11, 29, 5, 27, 7, 3, 12, 6, 2, 1,...
## $ `7/11/20` <dbl> 0, 15, 12, 2, 1, 1, 11, 29, 5, 27, 7, 3, 12, 6, 2, 1,...
## $ `7/12/20` <dbl> 0, 16, 12, 2, 1, 1, 11, 29, 5, 30, 7, 3, 12, 6, 2, 1,...
## $ `7/13/20` <dbl> 0, 16, 12, 2, 1, 1, 11, 29, 5, 30, 7, 3, 12, 6, 2, 1,...
## $ `7/14/20` <dbl> 0, 18, 12, 3, 2, 1, 11, 31, 6, 32, 7, 4, 12, 6, 2, 1,...
## $ `7/15/20` <dbl> 0, 19, 13, 3, 2, 1, 11, 31, 6, 32, 7, 4, 12, 6, 2, 1,...
## $ `7/16/20` <dbl> 0, 20, 14, 3, 2, 1, 11, 32, 6, 32, 7, 4, 12, 6, 2, 1,...
## $ `7/17/20` <dbl> 0, 21, 14, 3, 2, 1, 11, 33, 6, 32, 7, 4, 12, 6, 2, 1,...
## $ `7/18/20` <dbl> 0, 21, 15, 3, 2, 1, 11, 33, 6, 33, 7, 4, 12, 7, 2, 1,...
## $ `7/19/20` <dbl> 0, 21, 15, 3, 2, 1, 11, 33, 6, 33, 7, 4, 12, 7, 2, 1,...
## $ `7/20/20` <dbl> 0, 21, 15, 4, 2, 1, 11, 33, 6, 33, 7, 4, 12, 7, 2, 1,...
## $ `7/21/20` <dbl> 0, 21, 16, 4, 2, 1, 11, 34, 6, 33, 7, 4, 12, 7, 2, 1,...
## $ `7/22/20` <dbl> 0, 21, 16, 4, 2, 1, 12, 34, 6, 34, 7, 5, 12, 8, 2, 1,...
## $ `7/23/20` <dbl> 0, 21, 17, 4, 2, 1, 12, 35, 6, 34, 7, 5, 12, 9, 2, 1,...
## $ `7/24/20` <dbl> 0, 21, 18, 4, 2, 1, 12, 35, 6, 37, 8, 5, 12, 9, 3, 1,...
## $ `7/25/20` <dbl> 0, 21, 18, 4, 2, 1, 12, 35, 6, 37, 8, 5, 12, 9, 3, 1,...
## $ `7/26/20` <dbl> 0, 21, 18, 4, 2, 1, 12, 35, 6, 37, 8, 5, 12, 9, 3, 1,...
## $ `7/27/20` <dbl> 0, 21, 18, 4, 2, 1, 12, 36, 6, 38, 8, 6, 12, 9, 4, 1,...
## $ `7/28/20` <dbl> 0, 21, 18, 4, 2, 1, 12, 36, 6, 38, 8, 6, 12, 9, 4, 1,...
## $ `7/29/20` <dbl> 0, 21, 21, 4, 2, 3, 12, 36, 6, 38, 8, 6, 12, 9, 4, 1,...
## $ `7/30/20` <dbl> 0, 21, 21, 5, 2, 3, 12, 36, 8, 38, 8, 6, 12, 9, 5, 1,...
## $ `7/31/20` <dbl> 0, 21, 22, 5, 2, 3, 12, 36, 9, 38, 8, 6, 12, 9, 5, 1,...
## $ `8/1/20` <dbl> 0, 21, 22, 5, 2, 3, 12, 36, 9, 38, 8, 6, 12, 9, 5, 1,...
## $ `8/2/20` <dbl> 0, 21, 23, 5, 3, 3, 12, 36, 9, 38, 8, 7, 12, 9, 5, 1,...
## $ `8/3/20` <dbl> 0, 21, 24, 5, 3, 3, 12, 36, 9, 38, 8, 7, 12, 9, 5, 1,...
## $ `8/4/20` <dbl> 0, 21, 24, 5, 3, 3, 12, 36, 12, 38, 8, 7, 12, 9, 5, 1...
## $ `8/5/20` <dbl> 0, 22, 24, 5, 4, 3, 12, 36, 13, 38, 8, 7, 12, 9, 5, 1...
## $ `8/6/20` <dbl> 0, 22, 25, 5, 4, 3, 12, 36, 13, 38, 8, 7, 12, 9, 5, 1...
## $ `8/7/20` <dbl> 0, 22, 25, 5, 4, 3, 12, 36, 13, 38, 8, 8, 12, 9, 5, 1...
## $ `8/8/20` <dbl> 0, 22, 26, 5, 5, 4, 12, 37, 13, 38, 8, 8, 12, 9, 5, 1...
## $ `8/9/20` <dbl> 0, 22, 27, 5, 5, 4, 12, 37, 14, 38, 8, 8, 12, 9, 5, 1...
## $ `8/10/20` <dbl> 0, 22, 28, 5, 5, 4, 12, 37, 17, 38, 9, 9, 12, 10, 5, ...
## $ `8/11/20` <dbl> 0, 23, 32, 6, 6, 5, 12, 37, 19, 38, 9, 12, 12, 10, 5,...
## $ `8/12/20` <dbl> 0, 23, 32, 6, 6, 5, 12, 37, 20, 38, 9, 12, 12, 10, 5,...
## $ `8/13/20` <dbl> 0, 23, 32, 6, 6, 5, 12, 37, 20, 38, 9, 12, 12, 10, 5,...
## $ `8/14/20` <dbl> 0, 23, 32, 6, 6, 5, 12, 37, 20, 38, 9, 12, 12, 10, 5,...
## $ `8/15/20` <dbl> 0, 23, 32, 6, 6, 5, 12, 37, 20, 38, 9, 12, 12, 10, 5,...
## $ `8/16/20` <dbl> 0, 23, 32, 6, 6, 5, 12, 37, 20, 38, 9, 12, 12, 10, 5,...
## $ `8/17/20` <dbl> 0, 23, 32, 6, 6, 5, 14, 37, 23, 38, 9, 12, 12, 10, 5,...
## $ `8/18/20` <dbl> 0, 23, 33, 6, 6, 5, 14, 37, 25, 38, 9, 12, 12, 10, 5,...
## $ `8/19/20` <dbl> 0, 23, 33, 7, 6, 5, 14, 37, 25, 38, 9, 12, 12, 10, 5,...
## $ `8/20/20` <dbl> 0, 23, 34, 7, 6, 5, 14, 37, 25, 38, 10, 12, 12, 10, 5...
## $ `8/21/20` <dbl> 0, 23, 35, 7, 6, 6, 14, 37, 25, 38, 10, 13, 12, 10, 5...
## $ `8/22/20` <dbl> 0, 23, 35, 7, 6, 6, 14, 37, 25, 38, 10, 13, 12, 10, 6...
## $ `8/23/20` <dbl> 0, 23, 35, 7, 6, 6, 14, 37, 25, 38, 10, 13, 12, 10, 6...
## $ `8/24/20` <dbl> 0, 23, 35, 7, 6, 6, 14, 37, 27, 38, 10, 13, 12, 11, 6...
## $ `8/25/20` <dbl> 0, 23, 35, 7, 6, 6, 14, 37, 28, 39, 10, 13, 12, 11, 6...
## $ `8/26/20` <dbl> 0, 23, 36, 7, 6, 7, 14, 37, 28, 39, 10, 13, 12, 12, 6...
## $ `8/27/20` <dbl> 0, 23, 37, 7, 6, 7, 14, 37, 30, 39, 12, 13, 12, 13, 6...
## $ `8/28/20` <dbl> 0, 23, 39, 7, 6, 9, 14, 37, 32, 40, 12, 13, 12, 13, 6...
## $ `8/29/20` <dbl> 0, 23, 40, 7, 7, 9, 14, 37, 35, 40, 12, 13, 12, 14, 6...
## $ `8/30/20` <dbl> 0, 23, 40, 7, 7, 10, 14, 37, 35, 40, 12, 13, 12, 14, ...
## $ `8/31/20` <dbl> 0, 23, 42, 7, 8, 11, 14, 37, 36, 40, 12, 13, 12, 14, ...
## $ `9/1/20` <dbl> 0, 24, 42, 7, 8, 11, 14, 37, 38, 40, 12, 13, 12, 14, ...
## $ `9/2/20` <dbl> 0, 24, 42, 7, 8, 11, 14, 37, 38, 40, 12, 14, 12, 14, ...
## $ `9/3/20` <dbl> 0, 24, 44, 7, 8, 11, 14, 37, 38, 40, 12, 14, 12, 14, ...
## $ `9/4/20` <dbl> 0, 24, 46, 7, 9, 12, 14, 38, 38, 41, 12, 16, 12, 14, ...
## $ `9/5/20` <dbl> 0, 24, 46, 7, 9, 12, 14, 38, 38, 41, 12, 16, 12, 14, ...
## $ `9/6/20` <dbl> 0, 24, 46, 7, 9, 12, 14, 38, 38, 41, 12, 16, 12, 14, ...
## $ `9/7/20` <dbl> 0, 24, 46, 7, 9, 12, 14, 38, 38, 41, 12, 16, 12, 14, ...
## $ `9/8/20` <dbl> 0, 24, 46, 7, 9, 12, 14, 38, 38, 41, 12, 16, 12, 14, ...
## $ `9/9/20` <dbl> 0, 24, 46, 7, 9, 12, 14, 38, 38, 41, 12, 16, 12, 14, ...
## $ `9/10/20` <dbl> 0, 24, 46, 7, 9, 12, 14, 38, 38, 41, 13, 16, 12, 14, ...
## $ `9/11/20` <dbl> 0, 24, 47, 7, 9, 12, 14, 38, 38, 41, 14, 17, 12, 15, ...
## $ `9/12/20` <dbl> 0, 24, 47, 7, 9, 13, 14, 38, 38, 42, 14, 18, 12, 16, ...
## $ `9/13/20` <dbl> 0, 24, 47, 7, 9, 13, 14, 38, 38, 42, 14, 18, 12, 16, ...
## $ `9/14/20` <dbl> 0, 24, 47, 7, 9, 13, 14, 38, 38, 42, 14, 18, 12, 16, ...
## $ `9/15/20` <dbl> 0, 24, 47, 7, 9, 13, 14, 38, 38, 42, 15, 24, 12, 16, ...
## Observations: 760,410
## Variables: 6
## $ countyFIPS <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ countyName <chr> "Statewide Unallocated", "Statewide Unallocated", "State...
## $ state <chr> "AL", "AL", "AL", "AL", "AL", "AL", "AL", "AL", "AL", "A...
## $ stateFIPS <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,...
## $ date <date> 2020-01-22, 2020-01-23, 2020-01-24, 2020-01-25, 2020-01...
## $ cumDeaths <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...# STEP 11: Compare burden in old data and new data
bind_rows(burden_20200903_new, burden_20200917, .id="source") %>%
mutate(source=factor(case_when(source==1 ~ "2020-09-03", source==2 ~ "2020-09-17", TRUE ~ "Unknown"),
levels=c("2020-09-17", "2020-09-03", "Unknown")
)
) %>%
group_by(source, date) %>%
summarize(cumDeaths=sum(cumDeaths), cumCases=sum(cumCases)) %>%
pivot_longer(-c(source, date)) %>%
ggplot(aes(x=date, y=value/1000, group=source, color=source)) +
geom_line() +
facet_wrap(~c("cumCases"="Cases", "cumDeaths"="Deaths")[name], scales="free_y") +
scale_x_date(date_breaks="1 months", date_labels="%m") +
labs(y="Burden (000s)", title="US National Coronavirus Burden by Source")
# STEP 12: Show top-level findings by segment
plotBurdenData(burden_20200917, maxDate=maxDate_20200917, minPop=10000)
# STEP 13: Create county-level clusters (k-means, 5 clusters, minimum county population 25k)
clust_20200917_new <- prepClusterCounties(burdenFile=burden_20200917,
maxDate=maxDate_20200917,
minPop=25000,
hierarchical=FALSE,
minShape=3,
ratioDeathvsCase = 5,
ratioTotalvsShape = 0.5,
minDeath=100,
minCase=5000,
nCenters=5,
testCenters=1:25,
iter.max=20,
nstart=10,
seed=2009081450
)## # A tibble: 1 x 6
## cpm_mean_is0 dpm_mean_is0 cpm_mean_lt100 dpm_mean_lt100 cpm_mean_lt5000
## <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 0 0.0283 0 0.224 0.104
## # ... with 1 more variable: dpm_mean_lt5000 <dbl>
##
## Cluster means and counts
## 1 2 3 4 5
## . 503.00 484.00 73.00 219.00 312.00
## totalCases 0.77 0.39 1.40 1.14 0.56
## totalDeaths 1.56 0.46 9.47 4.69 1.97
## cases_3 0.01 0.02 0.06 0.01 0.02
## deaths_3 0.04 0.08 0.09 0.05 0.09
## cases_4 0.04 0.06 0.29 0.12 0.17
## deaths_4 0.32 0.53 1.61 0.76 1.46
## cases_5 0.05 0.07 0.15 0.12 0.16
## deaths_5 0.26 0.35 1.05 0.96 1.72
## cases_6 0.11 0.10 0.10 0.13 0.10
## deaths_6 0.33 0.51 0.58 0.63 0.66
## cases_7 0.34 0.23 0.20 0.29 0.20
## deaths_7 1.05 0.64 0.63 0.96 0.40
## cases_8 0.32 0.26 0.16 0.23 0.21
## deaths_8 2.09 0.52 0.78 1.22 0.42
## cases_9 0.14 0.16 0.05 0.08 0.11
## deaths_9 0.87 0.55 0.26 0.43 0.22# STEP 14: Assess the previous clusters on the updated data
helper_test_20200917 <- helperAssessCountyClusters(vecCluster=clustVec_county_20200903_new,
dfPop=clust_20200917_new$countyFiltered,
dfBurden=clust_20200917_new$countyFiltered,
thruLabel="Sep 15, 2020",
plotsTogether=TRUE,
showMap=TRUE,
clusterPlotsTogether=TRUE,
orderCluster=TRUE
)
##
## Recency is defined as 2020-08-17 through current
##
## Recency is defined as 2020-08-17 through current## Warning: Removed 1 rows containing missing values (geom_point).
# STEP 15: Create normalized data
cNorm_20200917 <- helperMakeNormData(helper_test_20200917)
# STEP 16: Assess lags for early pandemic and late pandemic
lagData_early_20200917 <- helperTestLags(cNorm_20200917, minDate="2020-03-01", maxDate="2020-05-31")
##
## The best lags are:
## # A tibble: 5 x 3
## cluster corr lag
## <fct> <dbl> <int>
## 1 5 0.971 2
## 2 2 0.809 4
## 3 1 0.937 5
## 4 4 0.972 7
## 5 3 0.989 8
lagData_late_20200917 <- helperTestLags(cNorm_20200917, minDate="2020-06-01", maxDate="2020-09-15")
##
## The best lags are:
## # A tibble: 5 x 3
## cluster corr lag
## <fct> <dbl> <int>
## 1 2 0.781 6
## 2 4 0.886 16
## 3 1 0.967 19
## 4 5 0.435 23
## 5 3 0.472 26
# STEP 17: Generate a list of key counties
keyCounties_20200917 <- helper_test_20200917 %>%
mutate(state=str_pad(state, width=5, side="left", pad="0")) %>%
filter(pop >= 100000) %>%
group_by(state, cluster) %>%
summarize(dpm=sum(dpm), pop=mean(pop)) %>%
group_by(cluster) %>%
top_n(n=3, wt=dpm) %>%
ungroup() %>%
arrange(cluster, -dpm) %>%
inner_join(select(usmap::countypop, -pop_2015), by=c("state"="fips")) %>%
mutate(countyName=paste0(cluster, " - ",
stringr::str_replace(county, "County|Parish", "("),
abbr,
")"
)
) %>%
select(-abbr, -county)
# STEP 18: Keep only key counties
cNorm_keyCounties_20200917 <- helper_test_20200917 %>%
mutate(state=str_pad(state, width=5, side="left", pad="0")) %>%
inner_join(select(keyCounties_20200917, state, countyName), by=c("state"="state")) %>%
helperMakeNormData(aggBy=c("countyName", "state", "cluster"))
# STEP 19: Create early and late lags for key counties
lagData_early_keycounties_20200917 <- helperTestLags(cNorm_keyCounties_20200917,
minDate="2020-03-01",
maxDate="2020-05-31",
aggBy=c("countyName", "state", "cluster"),
maxRatio=0.25
)
##
## The best lags are:
## # A tibble: 15 x 5
## countyName state cluster corr lag
## <chr> <chr> <fct> <dbl> <int>
## 1 1 - Anderson (SC) 45007 1 0.717 0
## 2 1 - Webb (TX) 48479 1 0.642 0
## 3 2 - Douglas (NE) 31055 2 0.517 0
## 4 2 - Pulaski (AR) 05119 2 0.644 0
## 5 1 - Highlands (FL) 12055 1 0.772 1
## 6 5 - Litchfield (CT) 09005 5 0.894 4
## 7 2 - DeKalb (IL) 17037 2 0.654 5
## 8 3 - Bronx (NY) 36005 3 0.962 6
## 9 3 - Kings (NY) 36047 3 0.945 8
## 10 3 - Queens (NY) 36081 3 0.943 8
## 11 4 - Yuma (AZ) 04027 4 0.956 11
## 12 5 - Gloucester (NJ) 34015 5 0.874 12
## 13 4 - Caddo (LA) 22017 4 0.785 13
## 14 4 - Hidalgo (TX) 48215 4 0.632 17
## 15 5 - Barnstable (MA) 25001 5 0.679 21## Warning: Removed 136 rows containing missing values (geom_path).## Warning: Removed 136 rows containing missing values (geom_path).
lagData_late_keycounties_20200917 <- helperTestLags(cNorm_keyCounties_20200917,
minDate="2020-06-01",
maxDate="2020-09-15",
aggBy=c("countyName", "state", "cluster"),
maxRatio=0.25
)
##
## The best lags are:
## # A tibble: 15 x 5
## countyName state cluster corr lag
## <chr> <chr> <fct> <dbl> <int>
## 1 5 - Litchfield (CT) 09005 5 0.179 6
## 2 1 - Highlands (FL) 12055 1 0.689 13
## 3 4 - Hidalgo (TX) 48215 4 0.758 16
## 4 2 - DeKalb (IL) 17037 2 0.130 19
## 5 2 - Douglas (NE) 31055 2 0.152 19
## 6 4 - Yuma (AZ) 04027 4 0.780 19
## 7 4 - Caddo (LA) 22017 4 0.312 24
## 8 2 - Pulaski (AR) 05119 2 0.436 25
## 9 1 - Anderson (SC) 45007 1 0.838 26
## 10 3 - Bronx (NY) 36005 3 0.755 26
## 11 3 - Kings (NY) 36047 3 0.929 26
## 12 3 - Queens (NY) 36081 3 0.734 26
## 13 5 - Gloucester (NJ) 34015 5 -0.412 29
## 14 1 - Webb (TX) 48479 1 0.738 30
## 15 5 - Barnstable (MA) 25001 5 0.560 30
# STEP 20: Run for only key counties
exploreTopCounties(helper_test_20200917, minDate="2020-03-01", maxDate="2020-05-31", nVar="pop", nKey=12)##
## *** KEY COUNTIES INCLUDE: ***
## # A tibble: 12 x 5
## state cluster countyName dpm pop
## <chr> <fct> <chr> <dbl> <dbl>
## 1 06037 5 5 - Los Angeles (CA) 625. 10039107
## 2 17031 4 4 - Cook (IL) 997. 5150233
## 3 48201 1 1 - Harris (TX) 518. 4713325
## 4 04013 4 4 - Maricopa (AZ) 707. 4485414
## 5 06073 2 2 - San Diego (CA) 222. 3338330
## 6 06059 1 1 - Orange (CA) 348. 3175692
## 7 12086 4 4 - Miami-Dade (FL) 1076. 2716940
## 8 48113 1 1 - Dallas (TX) 398. 2635516
## 9 36047 3 3 - Kings (NY) 2854. 2559903
## 10 06065 1 1 - Riverside (CA) 455. 2470546
## 11 32003 1 1 - Clark (NV) 568. 2266715
## 12 36081 3 3 - Queens (NY) 3208. 2253858
##
## The best lags are:
## # A tibble: 12 x 5
## countyName state cluster corr lag
## <chr> <chr> <fct> <dbl> <int>
## 1 1 - Clark (NV) 32003 1 0.799 0
## 2 1 - Dallas (TX) 48113 1 0.816 0
## 3 1 - Harris (TX) 48201 1 0.694 0
## 4 1 - Orange (CA) 06059 1 0.837 1
## 5 2 - San Diego (CA) 06073 2 0.861 1
## 6 4 - Cook (IL) 17031 4 0.972 4
## 7 4 - Maricopa (AZ) 04013 4 0.931 4
## 8 5 - Los Angeles (CA) 06037 5 0.874 4
## 9 3 - Kings (NY) 36047 3 0.945 8
## 10 3 - Queens (NY) 36081 3 0.943 8
## 11 1 - Riverside (CA) 06065 1 0.952 12
## 12 4 - Miami-Dade (FL) 12086 4 0.910 12## Warning: Removed 23 rows containing missing values (geom_path).## Warning: Removed 23 rows containing missing values (geom_path).
## # A tibble: 1,104 x 9
## # Groups: countyName, state, cluster [12]
## countyName state cluster date cpm7 dpm7 corr lag cpmlag
## <chr> <chr> <fct> <date> <dbl> <dbl> <dbl> <int> <dbl>
## 1 1 - Clark (NV) 32003 1 2020-03-01 0 0 0.799 0 0
## 2 1 - Dallas (TX) 48113 1 2020-03-01 0 0 0.816 0 0
## 3 1 - Harris (TX) 48201 1 2020-03-01 0 0 0.694 0 0
## 4 1 - Orange (CA) 06059 1 2020-03-01 0.0900 0 0.837 1 NA
## 5 1 - Riverside (CA) 06065 1 2020-03-01 0 0 0.952 12 NA
## 6 2 - San Diego (CA) 06073 2 2020-03-01 0 0 0.861 1 NA
## 7 3 - Kings (NY) 36047 3 2020-03-01 0 0 0.945 8 NA
## 8 3 - Queens (NY) 36081 3 2020-03-01 0 0 0.943 8 NA
## 9 4 - Cook (IL) 17031 4 2020-03-01 0.0555 0 0.972 4 NA
## 10 4 - Maricopa (AZ) 04013 4 2020-03-01 0 0 0.931 4 NA
## # ... with 1,094 more rowsexploreTopCounties(helper_test_20200917, minDate="2020-06-15", maxDate="2020-09-15", nVar="pop", nKey=12)##
## *** KEY COUNTIES INCLUDE: ***
## # A tibble: 12 x 5
## state cluster countyName dpm pop
## <chr> <fct> <chr> <dbl> <dbl>
## 1 06037 5 5 - Los Angeles (CA) 625. 10039107
## 2 17031 4 4 - Cook (IL) 997. 5150233
## 3 48201 1 1 - Harris (TX) 518. 4713325
## 4 04013 4 4 - Maricopa (AZ) 707. 4485414
## 5 06073 2 2 - San Diego (CA) 222. 3338330
## 6 06059 1 1 - Orange (CA) 348. 3175692
## 7 12086 4 4 - Miami-Dade (FL) 1076. 2716940
## 8 48113 1 1 - Dallas (TX) 398. 2635516
## 9 36047 3 3 - Kings (NY) 2854. 2559903
## 10 06065 1 1 - Riverside (CA) 455. 2470546
## 11 32003 1 1 - Clark (NV) 568. 2266715
## 12 36081 3 3 - Queens (NY) 3208. 2253858
##
## The best lags are:
## # A tibble: 12 x 5
## countyName state cluster corr lag
## <chr> <chr> <fct> <dbl> <int>
## 1 2 - San Diego (CA) 06073 2 0.771 5
## 2 3 - Kings (NY) 36047 3 0.773 12
## 3 5 - Los Angeles (CA) 06037 5 0.740 12
## 4 1 - Harris (TX) 48201 1 0.661 13
## 5 1 - Riverside (CA) 06065 1 0.505 16
## 6 4 - Maricopa (AZ) 04013 4 0.934 18
## 7 4 - Miami-Dade (FL) 12086 4 0.469 19
## 8 1 - Clark (NV) 32003 1 0.814 20
## 9 1 - Orange (CA) 06059 1 0.388 21
## 10 1 - Dallas (TX) 48113 1 0.609 30
## 11 3 - Queens (NY) 36081 3 0.768 30
## 12 4 - Cook (IL) 17031 4 -0.526 30
## # A tibble: 1,080 x 9
## # Groups: countyName, state, cluster [12]
## countyName state cluster date cpm7 dpm7 corr lag cpmlag
## <chr> <chr> <fct> <date> <dbl> <dbl> <dbl> <int> <dbl>
## 1 1 - Clark (NV) 32003 1 2020-06-15 90.4 0.756 0.814 20 NA
## 2 1 - Dallas (TX) 48113 1 2020-06-15 125. 1.52 0.609 30 NA
## 3 1 - Harris (TX) 48201 1 2020-06-15 79.0 0.940 0.661 13 NA
## 4 1 - Orange (CA) 06059 1 2020-06-15 58.7 2.16 0.388 21 NA
## 5 1 - Riverside (CA) 06065 1 2020-06-15 129. 1.79 0.505 16 NA
## 6 2 - San Diego (CA) 06073 2 2020-06-15 46.8 0.984 0.771 5 NA
## 7 3 - Kings (NY) 36047 3 2020-06-15 50.1 4.41 0.773 12 NA
## 8 3 - Queens (NY) 36081 3 2020-06-15 53.0 3.74 0.768 30 NA
## 9 4 - Cook (IL) 17031 4 2020-06-15 61.1 6.16 -0.526 30 NA
## 10 4 - Maricopa (AZ) 04013 4 2020-06-15 250. 2.10 0.934 18 NA
## # ... with 1,070 more rowsThe process is further converted to functional form as follows:
- Capability to either start the process from scratch or to leverage existing clusters at the state or county level
- Add the capability to compare a newly read file to an existing file
- Add the capability to show burden by existing segment after reading in an existing file
- Add the capability to prepare the data (create countyFiltered) without running the clustering proces
- Add the cumulative burden chart to helperAssessCountyClusters
New data are downloaded from 2020-10-03, and the updated code includes:
# STEP 1a: Define the locations for the population, cases, and deaths file
popFile <- "./RInputFiles/Coronavirus/covid_county_population_usafacts.csv"
caseFile_20201003 <- "./RInputFiles/Coronavirus/covid_confirmed_usafacts_downloaded_20201003.csv"
deathFile_20201003 <- "./RInputFiles/Coronavirus/covid_deaths_usafacts_downloaded_20201003.csv"
maxDate_20201003 <- "2020-09-30"
# STEP 1b: Read and pivot data from USA Facts; extract population data file as pop_usafacts
rawUSAFacts_20201003 <- readPivotUSAFacts(popFile=popFile,
caseFile=caseFile_20201003,
deathFile=deathFile_20201003,
unassignedDate=maxDate_20201003
)## Parsed with column specification:
## cols(
## countyFIPS = col_double(),
## `County Name` = col_character(),
## State = col_character(),
## population = col_double()
## )## Parsed with column specification:
## cols(
## .default = col_double(),
## `County Name` = col_character(),
## State = col_character()
## )## See spec(...) for full column specifications.## Parsed with column specification:
## cols(
## .default = col_double(),
## `County Name` = col_character(),
## State = col_character()
## )## See spec(...) for full column specifications.## Joining, by = c("state", "date", "unassigned")
pop_usafacts <- rawUSAFacts_20201003$pop
# STEP 2: Read case and death data (redundant), combine, and add population totals
# Add previous clusters by county rather than by state (function readUSAFacts updated)
burden_20201003 <- readUSAFacts(
caseFile=caseFile_20201003,
deathFile=deathFile_20201003,
oldFile=burden_20200903_new,
showBurdenMinPop=10000,
maxDate=maxDate_20201003,
stateClusters=NULL,
countyClusters=clustVec_county_20200903_new
)## Parsed with column specification:
## cols(
## .default = col_double(),
## `County Name` = col_character(),
## State = col_character()
## )## See spec(...) for full column specifications.## Observations: 3,195
## Variables: 258
## $ countyFIPS <dbl> 0, 1001, 1003, 1005, 1007, 1009, 1011, 1013, 1015, 10...
## $ `County Name` <chr> "Statewide Unallocated", "Autauga County", "Baldwin C...
## $ State <chr> "AL", "AL", "AL", "AL", "AL", "AL", "AL", "AL", "AL",...
## $ stateFIPS <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,...
## $ `1/22/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `1/23/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `1/24/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `1/25/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `1/26/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `1/27/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `1/28/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `1/29/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `1/30/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `1/31/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `2/1/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `2/2/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `2/3/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `2/4/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `2/5/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `2/6/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `2/7/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `2/8/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `2/9/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `2/10/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `2/11/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `2/12/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `2/13/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `2/14/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `2/15/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `2/16/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `2/17/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `2/18/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `2/19/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `2/20/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `2/21/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `2/22/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `2/23/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `2/24/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `2/25/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `2/26/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `2/27/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `2/28/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `2/29/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `3/1/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `3/2/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `3/3/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `3/4/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `3/5/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `3/6/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `3/7/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `3/8/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `3/9/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `3/10/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `3/11/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `3/12/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `3/13/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `3/14/20` <dbl> 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `3/15/20` <dbl> 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `3/16/20` <dbl> 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `3/17/20` <dbl> 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `3/18/20` <dbl> 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `3/19/20` <dbl> 0, 0, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `3/20/20` <dbl> 0, 0, 2, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `3/21/20` <dbl> 0, 0, 2, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `3/22/20` <dbl> 0, 0, 3, 0, 0, 0, 0, 0, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `3/23/20` <dbl> 0, 0, 3, 0, 0, 0, 0, 0, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `3/24/20` <dbl> 0, 1, 4, 0, 0, 0, 0, 0, 2, 5, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `3/25/20` <dbl> 0, 4, 4, 0, 0, 1, 0, 1, 2, 10, 1, 1, 0, 0, 1, 1, 0, 1...
## $ `3/26/20` <dbl> 0, 6, 5, 0, 0, 2, 2, 1, 2, 13, 1, 4, 1, 0, 1, 1, 0, 1...
## $ `3/27/20` <dbl> 0, 6, 5, 0, 0, 5, 2, 1, 3, 15, 1, 7, 1, 0, 1, 3, 0, 1...
## $ `3/28/20` <dbl> 0, 6, 10, 0, 0, 5, 2, 1, 3, 17, 1, 7, 1, 0, 2, 4, 0, ...
## $ `3/29/20` <dbl> 0, 6, 15, 0, 0, 5, 2, 1, 3, 27, 2, 8, 1, 0, 2, 5, 0, ...
## $ `3/30/20` <dbl> 0, 7, 18, 0, 2, 5, 2, 1, 9, 36, 2, 10, 2, 0, 2, 5, 0,...
## $ `3/31/20` <dbl> 0, 7, 19, 0, 3, 5, 2, 1, 9, 36, 2, 11, 3, 0, 2, 5, 0,...
## $ `4/1/20` <dbl> 0, 10, 23, 0, 3, 5, 2, 1, 11, 45, 2, 13, 4, 2, 3, 6, ...
## $ `4/2/20` <dbl> 0, 10, 25, 0, 4, 6, 2, 1, 12, 67, 4, 14, 4, 2, 7, 6, ...
## $ `4/3/20` <dbl> 0, 12, 28, 1, 4, 9, 2, 1, 20, 81, 5, 15, 4, 3, 8, 7, ...
## $ `4/4/20` <dbl> 0, 12, 29, 2, 4, 10, 2, 1, 21, 87, 6, 15, 4, 7, 9, 7,...
## $ `4/5/20` <dbl> 0, 12, 34, 2, 7, 10, 2, 1, 24, 90, 6, 18, 5, 9, 9, 7,...
## $ `4/6/20` <dbl> 0, 12, 38, 3, 7, 10, 2, 1, 38, 96, 6, 20, 6, 9, 9, 9,...
## $ `4/7/20` <dbl> 0, 12, 42, 3, 8, 10, 2, 2, 48, 102, 6, 20, 6, 10, 9, ...
## $ `4/8/20` <dbl> 0, 12, 49, 3, 9, 10, 3, 3, 52, 140, 7, 22, 6, 10, 11,...
## $ `4/9/20` <dbl> 0, 17, 59, 7, 11, 11, 4, 3, 54, 161, 7, 25, 6, 13, 11...
## $ `4/10/20` <dbl> 0, 17, 59, 9, 11, 12, 4, 3, 54, 171, 7, 27, 7, 13, 11...
## $ `4/11/20` <dbl> 0, 19, 66, 10, 13, 12, 4, 6, 57, 184, 7, 30, 9, 15, 1...
## $ `4/12/20` <dbl> 0, 19, 71, 10, 16, 13, 4, 7, 60, 200, 9, 30, 10, 19, ...
## $ `4/13/20` <dbl> 0, 19, 78, 10, 17, 15, 6, 8, 61, 212, 9, 33, 10, 19, ...
## $ `4/14/20` <dbl> 0, 23, 87, 11, 17, 16, 8, 8, 62, 216, 9, 33, 12, 21, ...
## $ `4/15/20` <dbl> 0, 25, 98, 13, 19, 17, 8, 11, 62, 227, 10, 37, 13, 22...
## $ `4/16/20` <dbl> 0, 25, 102, 14, 23, 18, 8, 11, 63, 234, 11, 37, 13, 2...
## $ `4/17/20` <dbl> 0, 25, 103, 15, 23, 20, 8, 13, 63, 236, 12, 37, 13, 2...
## $ `4/18/20` <dbl> 0, 25, 109, 18, 26, 20, 9, 13, 66, 240, 12, 39, 14, 2...
## $ `4/19/20` <dbl> 0, 27, 114, 20, 28, 21, 9, 14, 72, 246, 12, 42, 14, 2...
## $ `4/20/20` <dbl> 0, 28, 117, 22, 32, 22, 11, 14, 80, 257, 12, 43, 17, ...
## $ `4/21/20` <dbl> 0, 30, 123, 28, 32, 26, 11, 15, 83, 259, 12, 44, 18, ...
## $ `4/22/20` <dbl> 0, 32, 132, 29, 33, 29, 11, 17, 85, 270, 12, 46, 21, ...
## $ `4/23/20` <dbl> 0, 33, 143, 30, 33, 31, 12, 19, 88, 275, 12, 47, 22, ...
## $ `4/24/20` <dbl> 0, 36, 147, 32, 34, 31, 12, 21, 89, 282, 12, 49, 25, ...
## $ `4/25/20` <dbl> 0, 37, 154, 33, 35, 31, 12, 28, 90, 284, 12, 49, 27, ...
## $ `4/26/20` <dbl> 0, 37, 161, 33, 38, 34, 12, 32, 90, 285, 14, 51, 32, ...
## $ `4/27/20` <dbl> 0, 39, 168, 35, 42, 34, 12, 34, 90, 289, 14, 51, 39, ...
## $ `4/28/20` <dbl> 0, 40, 171, 37, 42, 34, 12, 45, 92, 290, 15, 52, 39, ...
## $ `4/29/20` <dbl> 0, 42, 173, 37, 42, 36, 12, 51, 93, 290, 15, 52, 39, ...
## $ `4/30/20` <dbl> 0, 42, 174, 39, 42, 37, 13, 53, 93, 290, 15, 52, 43, ...
## $ `5/1/20` <dbl> 0, 42, 175, 42, 42, 39, 14, 65, 93, 290, 15, 52, 49, ...
## $ `5/2/20` <dbl> 0, 45, 181, 43, 42, 40, 14, 92, 98, 294, 15, 54, 49, ...
## $ `5/3/20` <dbl> 0, 48, 187, 45, 42, 40, 14, 105, 105, 300, 16, 57, 49...
## $ `5/4/20` <dbl> 0, 53, 188, 45, 42, 40, 16, 114, 105, 302, 16, 58, 51...
## $ `5/5/20` <dbl> 0, 53, 189, 47, 43, 40, 18, 120, 114, 304, 17, 60, 54...
## $ `5/6/20` <dbl> 0, 58, 196, 47, 43, 42, 18, 130, 114, 306, 18, 61, 54...
## $ `5/7/20` <dbl> 0, 61, 205, 51, 44, 44, 18, 155, 120, 308, 18, 63, 56...
## $ `5/8/20` <dbl> 0, 67, 208, 53, 44, 44, 21, 162, 123, 311, 21, 63, 59...
## $ `5/9/20` <dbl> 0, 68, 216, 58, 45, 44, 22, 178, 124, 314, 22, 64, 61...
## $ `5/10/20` <dbl> 0, 74, 222, 59, 46, 44, 23, 189, 124, 316, 22, 65, 66...
## $ `5/11/20` <dbl> 0, 84, 224, 61, 46, 45, 26, 196, 125, 319, 24, 67, 67...
## $ `5/12/20` <dbl> 0, 91, 227, 67, 46, 45, 26, 224, 126, 324, 24, 69, 69...
## $ `5/13/20` <dbl> 0, 93, 231, 69, 46, 45, 28, 230, 127, 324, 24, 73, 72...
## $ `5/14/20` <dbl> 0, 103, 243, 74, 46, 45, 28, 249, 128, 326, 25, 74, 7...
## $ `5/15/20` <dbl> 0, 103, 244, 79, 49, 45, 32, 258, 129, 326, 26, 75, 8...
## $ `5/16/20` <dbl> 0, 110, 254, 79, 50, 45, 35, 271, 130, 328, 27, 77, 8...
## $ `5/17/20` <dbl> 0, 110, 254, 81, 50, 46, 35, 272, 130, 328, 27, 77, 8...
## $ `5/18/20` <dbl> 0, 120, 260, 85, 50, 47, 40, 285, 133, 329, 28, 79, 8...
## $ `5/19/20` <dbl> 0, 127, 262, 90, 51, 47, 52, 295, 133, 329, 29, 80, 9...
## $ `5/20/20` <dbl> 0, 136, 270, 96, 52, 47, 64, 312, 136, 330, 30, 83, 1...
## $ `5/21/20` <dbl> 0, 147, 270, 100, 52, 48, 71, 321, 136, 330, 31, 84, ...
## $ `5/22/20` <dbl> 0, 149, 271, 104, 55, 49, 89, 329, 137, 330, 33, 85, ...
## $ `5/23/20` <dbl> 0, 155, 273, 105, 58, 49, 105, 335, 138, 330, 33, 86,...
## $ `5/24/20` <dbl> 0, 159, 274, 110, 59, 49, 111, 344, 141, 336, 33, 87,...
## $ `5/25/20` <dbl> 0, 173, 276, 116, 62, 49, 141, 368, 147, 337, 33, 87,...
## $ `5/26/20` <dbl> 0, 189, 277, 122, 66, 51, 167, 380, 150, 338, 33, 90,...
## $ `5/27/20` <dbl> 0, 192, 281, 130, 71, 53, 176, 391, 152, 340, 33, 93,...
## $ `5/28/20` <dbl> 0, 205, 281, 132, 71, 58, 185, 392, 152, 349, 34, 97,...
## $ `5/29/20` <dbl> 0, 212, 282, 147, 71, 60, 201, 396, 153, 352, 36, 99,...
## $ `5/30/20` <dbl> 0, 216, 283, 150, 72, 61, 203, 402, 154, 353, 37, 100...
## $ `5/31/20` <dbl> 0, 220, 288, 164, 75, 62, 209, 410, 157, 355, 37, 100...
## $ `6/1/20` <dbl> 0, 233, 292, 172, 76, 63, 209, 414, 164, 358, 38, 103...
## $ `6/2/20` <dbl> 0, 238, 292, 175, 76, 63, 212, 416, 165, 358, 38, 104...
## $ `6/3/20` <dbl> 0, 239, 292, 177, 76, 63, 215, 419, 165, 359, 38, 105...
## $ `6/4/20` <dbl> 0, 241, 293, 177, 76, 63, 217, 421, 167, 360, 38, 107...
## $ `6/5/20` <dbl> 0, 248, 296, 183, 76, 64, 219, 431, 169, 363, 38, 108...
## $ `6/6/20` <dbl> 0, 259, 304, 190, 77, 70, 225, 442, 174, 373, 40, 108...
## $ `6/7/20` <dbl> 0, 265, 313, 193, 77, 72, 232, 449, 176, 378, 42, 110...
## $ `6/8/20` <dbl> 0, 272, 320, 197, 79, 73, 238, 455, 178, 383, 42, 111...
## $ `6/9/20` <dbl> 0, 282, 325, 199, 85, 75, 243, 464, 180, 391, 42, 117...
## $ `6/10/20` <dbl> 0, 295, 331, 208, 89, 79, 248, 471, 182, 401, 42, 118...
## $ `6/11/20` <dbl> 0, 312, 343, 214, 93, 87, 253, 484, 184, 417, 42, 121...
## $ `6/12/20` <dbl> 0, 323, 353, 221, 97, 95, 258, 499, 188, 427, 46, 122...
## $ `6/13/20` <dbl> 0, 331, 361, 226, 100, 102, 276, 517, 190, 438, 47, 1...
## $ `6/14/20` <dbl> 0, 357, 364, 234, 104, 110, 302, 536, 195, 453, 51, 1...
## $ `6/15/20` <dbl> 0, 368, 383, 238, 111, 116, 307, 544, 204, 475, 53, 1...
## $ `6/16/20` <dbl> 0, 373, 389, 245, 116, 121, 310, 551, 206, 485, 53, 1...
## $ `6/17/20` <dbl> 0, 375, 392, 251, 118, 123, 313, 554, 208, 486, 53, 1...
## $ `6/18/20` <dbl> 0, 400, 401, 263, 121, 130, 320, 566, 210, 501, 55, 1...
## $ `6/19/20` <dbl> 0, 411, 413, 266, 126, 139, 320, 569, 210, 507, 58, 1...
## $ `6/20/20` <dbl> 0, 431, 420, 272, 126, 143, 327, 572, 211, 516, 58, 1...
## $ `6/21/20` <dbl> 0, 434, 430, 272, 127, 149, 327, 576, 213, 521, 58, 1...
## $ `6/22/20` <dbl> 0, 442, 437, 277, 129, 153, 328, 578, 215, 528, 58, 1...
## $ `6/23/20` <dbl> 0, 453, 450, 280, 135, 159, 329, 581, 216, 534, 58, 1...
## $ `6/24/20` <dbl> 0, 469, 464, 288, 141, 168, 336, 584, 220, 543, 58, 1...
## $ `6/25/20` <dbl> 0, 479, 477, 305, 149, 176, 351, 588, 233, 549, 64, 1...
## $ `6/26/20` <dbl> 0, 488, 515, 312, 153, 184, 351, 594, 236, 559, 68, 1...
## $ `6/27/20` <dbl> 0, 498, 555, 317, 161, 188, 358, 600, 245, 561, 69, 2...
## $ `6/28/20` <dbl> 0, 503, 575, 317, 162, 189, 358, 602, 245, 561, 70, 2...
## $ `6/29/20` <dbl> 0, 527, 643, 322, 165, 199, 365, 605, 269, 585, 73, 2...
## $ `6/30/20` <dbl> 0, 537, 680, 325, 170, 208, 365, 607, 276, 590, 74, 2...
## $ `7/1/20` <dbl> 0, 553, 703, 326, 174, 218, 367, 607, 278, 595, 77, 2...
## $ `7/2/20` <dbl> 0, 561, 751, 335, 179, 222, 369, 610, 288, 611, 82, 2...
## $ `7/3/20` <dbl> 0, 568, 845, 348, 189, 230, 372, 625, 330, 625, 88, 2...
## $ `7/4/20` <dbl> 0, 591, 863, 350, 190, 234, 373, 626, 340, 637, 88, 2...
## $ `7/5/20` <dbl> 0, 615, 881, 352, 193, 239, 373, 634, 362, 642, 100, ...
## $ `7/6/20` <dbl> 0, 618, 911, 356, 197, 247, 373, 634, 384, 655, 105, ...
## $ `7/7/20` <dbl> 0, 644, 997, 360, 199, 255, 373, 634, 395, 656, 106, ...
## $ `7/8/20` <dbl> 0, 651, 1056, 366, 201, 262, 374, 639, 411, 660, 114,...
## $ `7/9/20` <dbl> 0, 661, 1131, 371, 211, 282, 375, 646, 445, 672, 115,...
## $ `7/10/20` <dbl> 0, 670, 1187, 381, 218, 292, 381, 648, 465, 679, 118,...
## $ `7/11/20` <dbl> 0, 684, 1224, 398, 224, 307, 382, 654, 500, 690, 128,...
## $ `7/12/20` <dbl> 0, 706, 1294, 403, 228, 331, 383, 655, 526, 693, 129,...
## $ `7/13/20` <dbl> 0, 728, 1359, 413, 231, 350, 383, 660, 566, 702, 136,...
## $ `7/14/20` <dbl> 0, 746, 1414, 428, 236, 366, 385, 661, 589, 712, 140,...
## $ `7/15/20` <dbl> 0, 756, 1518, 441, 242, 389, 386, 664, 655, 718, 145,...
## $ `7/16/20` <dbl> 0, 780, 1599, 459, 247, 424, 389, 669, 675, 731, 152,...
## $ `7/17/20` <dbl> 0, 789, 1689, 463, 255, 440, 393, 672, 720, 742, 157,...
## $ `7/18/20` <dbl> 0, 827, 1819, 483, 264, 458, 397, 678, 741, 756, 165,...
## $ `7/19/20` <dbl> 0, 842, 1937, 495, 269, 482, 398, 686, 785, 762, 173,...
## $ `7/20/20` <dbl> 0, 857, 2013, 503, 279, 507, 400, 689, 832, 767, 179,...
## $ `7/21/20` <dbl> 0, 865, 2102, 514, 283, 524, 401, 695, 869, 774, 182,...
## $ `7/22/20` <dbl> 0, 886, 2196, 518, 287, 547, 407, 701, 891, 782, 184,...
## $ `7/23/20` <dbl> 0, 905, 2461, 534, 289, 585, 408, 706, 934, 789, 193,...
## $ `7/24/20` <dbl> 0, 921, 2513, 539, 303, 615, 411, 711, 999, 797, 205,...
## $ `7/25/20` <dbl> 0, 932, 2662, 552, 318, 637, 414, 720, 1062, 810, 207...
## $ `7/26/20` <dbl> 0, 942, 2708, 562, 324, 646, 415, 724, 1113, 821, 209...
## $ `7/27/20` <dbl> 0, 965, 2770, 569, 334, 669, 416, 730, 1194, 825, 220...
## $ `7/28/20` <dbl> 0, 974, 2835, 575, 337, 675, 429, 734, 1243, 836, 221...
## $ `7/29/20` <dbl> 0, 974, 2835, 575, 338, 675, 429, 734, 1244, 836, 221...
## $ `7/30/20` <dbl> 0, 1002, 3028, 585, 352, 731, 435, 747, 1336, 848, 23...
## $ `7/31/20` <dbl> 0, 1015, 3101, 598, 363, 767, 437, 753, 1450, 859, 23...
## $ `8/1/20` <dbl> 0, 1030, 3142, 602, 368, 792, 443, 757, 1480, 861, 24...
## $ `8/2/20` <dbl> 0, 1052, 3223, 610, 372, 813, 445, 765, 1580, 868, 25...
## $ `8/3/20` <dbl> 0, 1066, 3265, 612, 382, 830, 446, 766, 1612, 875, 26...
## $ `8/4/20` <dbl> 0, 1073, 3320, 614, 389, 836, 449, 766, 1646, 882, 26...
## $ `8/5/20` <dbl> 0, 1073, 3380, 615, 392, 839, 452, 769, 1683, 886, 27...
## $ `8/6/20` <dbl> 0, 1096, 3438, 619, 421, 874, 458, 771, 1741, 893, 28...
## $ `8/7/20` <dbl> 0, 1113, 3504, 624, 424, 909, 462, 774, 1777, 899, 29...
## $ `8/8/20` <dbl> 0, 1134, 3564, 628, 434, 923, 471, 773, 1836, 904, 29...
## $ `8/9/20` <dbl> 0, 1215, 3606, 630, 446, 934, 472, 779, 1860, 906, 30...
## $ `8/10/20` <dbl> 0, 1215, 3714, 631, 450, 947, 474, 782, 1883, 909, 30...
## $ `8/11/20` <dbl> 0, 1215, 3736, 643, 455, 958, 489, 785, 1914, 916, 30...
## $ `8/12/20` <dbl> 0, 1241, 3776, 646, 464, 967, 500, 788, 1935, 918, 31...
## $ `8/13/20` <dbl> 0, 1250, 3813, 651, 469, 977, 501, 790, 1959, 919, 32...
## $ `8/14/20` <dbl> 0, 1252, 3860, 656, 477, 989, 502, 796, 1975, 922, 32...
## $ `8/15/20` <dbl> 0, 1262, 3909, 663, 483, 996, 503, 807, 2019, 925, 33...
## $ `8/16/20` <dbl> 0, 1273, 3948, 671, 483, 1005, 504, 811, 2037, 927, 3...
## $ `8/17/20` <dbl> 0, 1274, 3960, 672, 488, 1008, 504, 814, 2055, 928, 3...
## $ `8/18/20` <dbl> 0, 1291, 3977, 674, 490, 1034, 512, 814, 2107, 937, 3...
## $ `8/19/20` <dbl> 0, 1293, 4002, 683, 503, 1049, 530, 814, 2159, 941, 3...
## $ `8/20/20` <dbl> 0, 1293, 4035, 690, 507, 1077, 534, 814, 2214, 949, 3...
## $ `8/21/20` <dbl> 0, 1293, 4054, 690, 509, 1083, 534, 814, 2228, 952, 3...
## $ `8/22/20` <dbl> 0, 1322, 4115, 699, 516, 1096, 536, 822, 2276, 957, 3...
## $ `8/23/20` <dbl> 0, 1324, 4147, 702, 523, 1099, 536, 824, 2286, 958, 3...
## $ `8/24/20` <dbl> 0, 1351, 4167, 720, 526, 1135, 536, 825, 2327, 971, 3...
## $ `8/25/20` <dbl> 0, 1355, 4190, 724, 527, 1160, 536, 826, 2345, 973, 3...
## $ `8/26/20` <dbl> 0, 1366, 4265, 732, 530, 1195, 537, 833, 2400, 983, 3...
## $ `8/27/20` <dbl> 0, 1377, 4311, 739, 533, 1213, 538, 839, 2413, 1011, ...
## $ `8/28/20` <dbl> 0, 1389, 4347, 745, 535, 1219, 541, 840, 2443, 1017, ...
## $ `8/29/20` <dbl> 0, 1400, 4424, 753, 540, 1248, 546, 855, 2499, 1024, ...
## $ `8/30/20` <dbl> 0, 1438, 4525, 757, 550, 1277, 550, 864, 2533, 1027, ...
## $ `8/31/20` <dbl> 0, 1442, 4545, 757, 554, 1287, 551, 866, 2567, 1033, ...
## $ `9/1/20` <dbl> 0, 1452, 4568, 764, 558, 1303, 559, 871, 2619, 1041, ...
## $ `9/2/20` <dbl> 0, 1452, 4583, 768, 562, 1308, 561, 872, 2633, 1045, ...
## $ `9/3/20` <dbl> 0, 1466, 4628, 771, 564, 1336, 563, 874, 2678, 1046, ...
## $ `9/4/20` <dbl> 0, 1475, 4654, 776, 570, 1361, 563, 881, 2747, 1054, ...
## $ `9/5/20` <dbl> 0, 1492, 4686, 776, 576, 1376, 566, 886, 2830, 1059, ...
## $ `9/6/20` <dbl> 0, 1498, 4713, 777, 581, 1379, 568, 890, 2842, 1061, ...
## $ `9/7/20` <dbl> 0, 1504, 4730, 778, 583, 1384, 568, 892, 2877, 1063, ...
## $ `9/8/20` <dbl> 0, 1508, 4757, 778, 589, 1390, 568, 892, 2891, 1064, ...
## $ `9/9/20` <dbl> 0, 1522, 4787, 778, 591, 1401, 570, 895, 2907, 1068, ...
## $ `9/10/20` <dbl> 0, 1544, 4833, 785, 594, 1430, 572, 896, 2958, 1076, ...
## $ `9/11/20` <dbl> 0, 1551, 4886, 786, 602, 1441, 573, 896, 2988, 1088, ...
## $ `9/12/20` <dbl> 0, 1565, 4922, 792, 604, 1446, 574, 898, 3047, 1094, ...
## $ `9/13/20` <dbl> 0, 1576, 4959, 794, 607, 1453, 580, 899, 3093, 1094, ...
## $ `9/14/20` <dbl> 0, 1585, 4978, 801, 610, 1464, 580, 900, 3110, 1097, ...
## $ `9/15/20` <dbl> 0, 1601, 4992, 806, 611, 1475, 581, 901, 3127, 1102, ...
## $ `9/16/20` <dbl> 0, 1619, 5003, 809, 612, 1487, 583, 901, 3165, 1106, ...
## $ `9/17/20` <dbl> 0, 1624, 5021, 809, 617, 1504, 585, 902, 3211, 1106, ...
## $ `9/18/20` <dbl> 0, 1664, 5033, 824, 619, 1527, 585, 906, 3249, 1117, ...
## $ `9/19/20` <dbl> 0, 1673, 5047, 830, 628, 1542, 585, 908, 3320, 1123, ...
## $ `9/20/20` <dbl> 0, 1690, 5061, 835, 632, 1551, 587, 909, 3338, 1130, ...
## $ `9/21/20` <dbl> 0, 1691, 5087, 838, 635, 1560, 591, 911, 3374, 1132, ...
## $ `9/22/20` <dbl> 0, 1714, 5124, 848, 635, 1573, 593, 911, 3390, 1140, ...
## $ `9/23/20` <dbl> 0, 1715, 5141, 851, 638, 1580, 597, 911, 3401, 1144, ...
## $ `9/24/20` <dbl> 1074, 1715, 5141, 851, 638, 1580, 597, 911, 3401, 114...
## $ `9/25/20` <dbl> 0, 1757, 5456, 873, 652, 1608, 599, 912, 3499, 1161, ...
## $ `9/26/20` <dbl> 0, 1764, 5477, 882, 654, 1611, 604, 912, 3515, 1164, ...
## $ `9/27/20` <dbl> 0, 1773, 5526, 885, 656, 1617, 606, 913, 3534, 1168, ...
## $ `9/28/20` <dbl> 0, 1785, 5588, 886, 657, 1618, 607, 914, 3548, 1172, ...
## $ `9/29/20` <dbl> 0, 1787, 5606, 886, 658, 1621, 607, 917, 3556, 1175, ...
## $ `9/30/20` <dbl> 0, 1791, 5640, 896, 664, 1629, 610, 917, 3569, 1179, ...
## $ `10/1/20` <dbl> 0, 1798, 5997, 898, 672, 1634, 612, 919, 3587, 1181, ...
## Observations: 811,530
## Variables: 6
## $ countyFIPS <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ countyName <chr> "Statewide Unallocated", "Statewide Unallocated", "State...
## $ state <chr> "AL", "AL", "AL", "AL", "AL", "AL", "AL", "AL", "AL", "A...
## $ stateFIPS <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,...
## $ date <date> 2020-01-22, 2020-01-23, 2020-01-24, 2020-01-25, 2020-01...
## $ cumCases <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...## Parsed with column specification:
## cols(
## .default = col_double(),
## `County Name` = col_character(),
## State = col_character()
## )
## See spec(...) for full column specifications.## Observations: 3,195
## Variables: 258
## $ countyFIPS <dbl> 0, 1001, 1003, 1005, 1007, 1009, 1011, 1013, 1015, 10...
## $ `County Name` <chr> "Statewide Unallocated", "Autauga County", "Baldwin C...
## $ State <chr> "AL", "AL", "AL", "AL", "AL", "AL", "AL", "AL", "AL",...
## $ stateFIPS <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,...
## $ `1/22/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `1/23/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `1/24/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `1/25/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `1/26/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `1/27/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `1/28/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `1/29/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `1/30/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `1/31/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `2/1/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `2/2/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `2/3/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `2/4/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `2/5/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `2/6/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `2/7/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `2/8/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `2/9/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `2/10/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `2/11/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `2/12/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `2/13/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `2/14/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `2/15/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `2/16/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `2/17/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `2/18/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `2/19/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `2/20/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `2/21/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `2/22/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `2/23/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `2/24/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `2/25/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `2/26/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `2/27/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `2/28/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `2/29/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `3/1/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `3/2/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `3/3/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `3/4/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `3/5/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `3/6/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `3/7/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `3/8/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `3/9/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `3/10/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `3/11/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `3/12/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `3/13/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `3/14/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `3/15/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `3/16/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `3/17/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `3/18/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `3/19/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `3/20/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `3/21/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `3/22/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `3/23/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `3/24/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `3/25/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `3/26/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `3/27/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `3/28/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `3/29/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `3/30/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `3/31/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `4/1/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `4/2/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `4/3/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `4/4/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `4/5/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `4/6/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `4/7/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `4/8/20` <dbl> 0, 1, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `4/9/20` <dbl> 0, 1, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `4/10/20` <dbl> 0, 1, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 1,...
## $ `4/11/20` <dbl> 0, 1, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 1,...
## $ `4/12/20` <dbl> 0, 1, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 1,...
## $ `4/13/20` <dbl> 0, 1, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 1,...
## $ `4/14/20` <dbl> 0, 1, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 1,...
## $ `4/15/20` <dbl> 0, 1, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 1,...
## $ `4/16/20` <dbl> 0, 1, 0, 0, 0, 0, 0, 0, 0, 9, 0, 0, 0, 0, 0, 0, 0, 1,...
## $ `4/17/20` <dbl> 0, 1, 0, 0, 0, 0, 0, 0, 1, 11, 0, 0, 0, 0, 0, 0, 0, 1...
## $ `4/18/20` <dbl> 0, 1, 1, 0, 0, 0, 0, 0, 2, 11, 0, 0, 0, 0, 0, 0, 0, 1...
## $ `4/19/20` <dbl> 0, 1, 1, 0, 0, 0, 0, 0, 2, 11, 0, 0, 0, 0, 0, 0, 0, 1...
## $ `4/20/20` <dbl> 0, 1, 1, 0, 0, 0, 0, 0, 2, 11, 0, 0, 0, 0, 0, 0, 0, 1...
## $ `4/21/20` <dbl> 0, 1, 2, 0, 0, 0, 0, 0, 3, 13, 0, 0, 0, 1, 0, 0, 0, 1...
## $ `4/22/20` <dbl> 0, 1, 2, 0, 0, 0, 0, 0, 3, 16, 0, 0, 0, 1, 0, 1, 0, 1...
## $ `4/23/20` <dbl> 0, 2, 2, 0, 0, 0, 0, 0, 3, 16, 0, 1, 0, 1, 1, 1, 0, 1...
## $ `4/24/20` <dbl> 0, 2, 2, 0, 0, 0, 0, 0, 3, 17, 0, 1, 0, 1, 1, 1, 0, 2...
## $ `4/25/20` <dbl> 0, 2, 2, 0, 0, 0, 0, 0, 3, 18, 0, 1, 0, 1, 1, 1, 0, 2...
## $ `4/26/20` <dbl> 0, 2, 2, 0, 0, 0, 0, 1, 3, 18, 0, 1, 0, 1, 1, 1, 0, 2...
## $ `4/27/20` <dbl> 0, 3, 2, 0, 0, 0, 0, 1, 3, 18, 0, 1, 0, 1, 1, 1, 0, 2...
## $ `4/28/20` <dbl> 0, 3, 2, 0, 0, 0, 0, 1, 3, 19, 0, 1, 0, 1, 1, 1, 0, 2...
## $ `4/29/20` <dbl> 0, 3, 2, 1, 0, 0, 0, 1, 3, 21, 0, 1, 0, 1, 1, 1, 0, 2...
## $ `4/30/20` <dbl> 0, 3, 3, 1, 0, 0, 0, 1, 3, 21, 0, 1, 0, 1, 1, 1, 0, 2...
## $ `5/1/20` <dbl> 0, 3, 4, 1, 0, 0, 0, 1, 3, 21, 0, 1, 0, 1, 1, 1, 0, 2...
## $ `5/2/20` <dbl> 0, 3, 4, 1, 0, 0, 0, 1, 3, 21, 0, 1, 0, 1, 1, 1, 0, 2...
## $ `5/3/20` <dbl> 0, 3, 4, 1, 0, 0, 0, 1, 3, 21, 0, 1, 0, 1, 1, 1, 0, 2...
## $ `5/4/20` <dbl> 0, 3, 4, 1, 0, 0, 0, 1, 3, 21, 0, 1, 0, 1, 1, 1, 0, 2...
## $ `5/5/20` <dbl> 0, 3, 5, 1, 0, 0, 0, 2, 3, 21, 0, 1, 0, 1, 1, 1, 0, 2...
## $ `5/6/20` <dbl> 0, 3, 5, 1, 0, 0, 1, 2, 3, 21, 0, 1, 0, 1, 1, 1, 0, 2...
## $ `5/7/20` <dbl> 0, 3, 5, 1, 0, 0, 1, 2, 3, 21, 0, 1, 0, 1, 1, 1, 0, 2...
## $ `5/8/20` <dbl> 0, 3, 5, 1, 1, 0, 1, 3, 3, 21, 0, 1, 0, 1, 1, 1, 0, 2...
## $ `5/9/20` <dbl> 0, 3, 5, 1, 1, 0, 1, 6, 3, 21, 0, 1, 0, 1, 1, 1, 0, 2...
## $ `5/10/20` <dbl> 0, 3, 5, 1, 1, 0, 1, 6, 3, 21, 0, 1, 0, 1, 1, 1, 0, 2...
## $ `5/11/20` <dbl> 0, 3, 6, 1, 1, 0, 1, 6, 3, 21, 0, 1, 0, 1, 1, 1, 0, 2...
## $ `5/12/20` <dbl> 0, 3, 7, 1, 1, 0, 1, 6, 3, 21, 0, 1, 1, 1, 1, 1, 1, 2...
## $ `5/13/20` <dbl> 0, 3, 7, 1, 1, 0, 1, 6, 3, 22, 0, 1, 2, 1, 1, 1, 1, 2...
## $ `5/14/20` <dbl> 0, 3, 8, 1, 1, 0, 1, 8, 3, 22, 0, 1, 3, 2, 2, 1, 1, 2...
## $ `5/15/20` <dbl> 0, 3, 8, 1, 1, 0, 1, 9, 3, 22, 0, 1, 3, 2, 2, 1, 1, 2...
## $ `5/16/20` <dbl> 0, 3, 8, 1, 1, 0, 1, 9, 3, 22, 0, 1, 3, 2, 2, 1, 1, 2...
## $ `5/17/20` <dbl> 0, 3, 8, 1, 1, 1, 1, 9, 3, 22, 0, 1, 3, 2, 2, 1, 1, 2...
## $ `5/18/20` <dbl> 0, 3, 8, 1, 1, 1, 1, 10, 3, 22, 0, 1, 3, 2, 2, 1, 1, ...
## $ `5/19/20` <dbl> 0, 3, 8, 1, 1, 1, 1, 10, 3, 22, 0, 1, 3, 2, 2, 1, 1, ...
## $ `5/20/20` <dbl> 0, 3, 8, 1, 1, 1, 1, 11, 3, 23, 0, 1, 3, 2, 2, 1, 1, ...
## $ `5/21/20` <dbl> 0, 3, 8, 1, 1, 1, 1, 11, 3, 23, 0, 1, 3, 2, 2, 1, 1, ...
## $ `5/22/20` <dbl> 0, 3, 9, 1, 1, 1, 1, 11, 3, 23, 2, 1, 4, 2, 2, 1, 1, ...
## $ `5/23/20` <dbl> 0, 3, 9, 1, 1, 1, 1, 11, 3, 23, 2, 1, 4, 2, 2, 1, 1, ...
## $ `5/24/20` <dbl> 0, 3, 9, 1, 1, 1, 1, 12, 3, 23, 2, 1, 4, 2, 2, 1, 1, ...
## $ `5/25/20` <dbl> 0, 3, 9, 1, 1, 1, 3, 12, 3, 24, 2, 1, 4, 2, 2, 1, 1, ...
## $ `5/26/20` <dbl> 0, 3, 9, 1, 1, 1, 3, 13, 3, 24, 2, 1, 7, 2, 2, 1, 1, ...
## $ `5/27/20` <dbl> 0, 3, 9, 1, 1, 1, 3, 13, 3, 24, 2, 1, 7, 2, 2, 1, 1, ...
## $ `5/28/20` <dbl> 0, 3, 9, 1, 1, 1, 4, 15, 3, 24, 2, 1, 8, 2, 2, 1, 1, ...
## $ `5/29/20` <dbl> 0, 3, 9, 1, 1, 1, 4, 16, 3, 24, 3, 1, 8, 2, 2, 1, 1, ...
## $ `5/30/20` <dbl> 0, 4, 9, 1, 1, 1, 4, 17, 3, 25, 3, 1, 8, 2, 2, 1, 1, ...
## $ `5/31/20` <dbl> 0, 4, 9, 1, 1, 1, 5, 18, 3, 25, 3, 1, 8, 2, 2, 1, 1, ...
## $ `6/1/20` <dbl> 0, 5, 9, 1, 1, 1, 6, 18, 3, 25, 3, 1, 10, 2, 2, 1, 1,...
## $ `6/2/20` <dbl> 0, 5, 9, 1, 1, 1, 6, 18, 3, 26, 3, 1, 10, 2, 2, 1, 1,...
## $ `6/3/20` <dbl> 0, 5, 9, 1, 1, 1, 6, 18, 3, 26, 3, 1, 10, 2, 2, 1, 1,...
## $ `6/4/20` <dbl> 0, 5, 9, 1, 1, 1, 6, 18, 3, 26, 3, 1, 10, 2, 2, 1, 1,...
## $ `6/5/20` <dbl> 0, 5, 9, 1, 1, 1, 7, 21, 3, 26, 3, 1, 10, 2, 2, 1, 1,...
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## $ `8/17/20` <dbl> 0, 23, 32, 6, 6, 5, 14, 37, 23, 38, 9, 12, 12, 10, 5,...
## $ `8/18/20` <dbl> 0, 23, 33, 6, 6, 5, 14, 37, 25, 38, 9, 12, 12, 10, 5,...
## $ `8/19/20` <dbl> 0, 23, 33, 7, 6, 5, 14, 37, 25, 38, 9, 12, 12, 10, 5,...
## $ `8/20/20` <dbl> 0, 23, 34, 7, 6, 5, 14, 37, 25, 38, 10, 12, 12, 10, 5...
## $ `8/21/20` <dbl> 0, 23, 35, 7, 6, 6, 14, 37, 25, 38, 10, 13, 12, 10, 5...
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## $ `8/30/20` <dbl> 0, 23, 40, 7, 7, 10, 14, 37, 35, 40, 12, 13, 12, 14, ...
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## $ `9/1/20` <dbl> 0, 24, 42, 7, 8, 11, 14, 37, 38, 40, 12, 13, 12, 14, ...
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## $ `9/5/20` <dbl> 0, 24, 46, 7, 9, 12, 14, 38, 38, 41, 12, 16, 12, 14, ...
## $ `9/6/20` <dbl> 0, 24, 46, 7, 9, 12, 14, 38, 38, 41, 12, 16, 12, 14, ...
## $ `9/7/20` <dbl> 0, 24, 46, 7, 9, 12, 14, 38, 38, 41, 12, 16, 12, 14, ...
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## $ `9/10/20` <dbl> 0, 24, 46, 7, 9, 12, 14, 38, 38, 41, 13, 16, 12, 14, ...
## $ `9/11/20` <dbl> 0, 24, 47, 7, 9, 12, 14, 38, 38, 41, 14, 17, 12, 15, ...
## $ `9/12/20` <dbl> 0, 24, 47, 7, 9, 13, 14, 38, 38, 42, 14, 18, 12, 16, ...
## $ `9/13/20` <dbl> 0, 24, 47, 7, 9, 13, 14, 38, 38, 42, 14, 18, 12, 16, ...
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## $ `9/15/20` <dbl> 0, 24, 47, 7, 9, 13, 14, 38, 38, 42, 15, 24, 12, 16, ...
## $ `9/16/20` <dbl> 0, 24, 47, 7, 9, 13, 14, 38, 38, 42, 15, 24, 12, 16, ...
## $ `9/17/20` <dbl> 0, 24, 48, 7, 9, 13, 14, 38, 39, 42, 15, 25, 12, 17, ...
## $ `9/18/20` <dbl> 0, 24, 48, 7, 10, 14, 14, 38, 39, 42, 15, 27, 12, 17,...
## $ `9/19/20` <dbl> 0, 24, 49, 7, 10, 14, 14, 38, 39, 42, 15, 27, 12, 17,...
## $ `9/20/20` <dbl> 0, 24, 49, 7, 10, 14, 14, 38, 39, 42, 15, 27, 12, 17,...
## $ `9/21/20` <dbl> 0, 24, 49, 7, 10, 14, 14, 38, 39, 42, 15, 27, 12, 17,...
## $ `9/22/20` <dbl> 0, 25, 49, 7, 10, 14, 14, 38, 40, 42, 15, 28, 12, 17,...
## $ `9/23/20` <dbl> 0, 25, 49, 7, 10, 15, 14, 39, 40, 42, 15, 30, 12, 17,...
## $ `9/24/20` <dbl> 0, 25, 50, 7, 10, 15, 14, 39, 42, 42, 13, 30, 12, 16,...
## $ `9/25/20` <dbl> 0, 25, 50, 7, 10, 15, 14, 39, 44, 42, 13, 30, 12, 16,...
## $ `9/26/20` <dbl> 0, 25, 50, 7, 10, 15, 14, 39, 44, 42, 13, 30, 12, 16,...
## $ `9/27/20` <dbl> 0, 25, 50, 7, 10, 15, 14, 39, 44, 42, 13, 30, 12, 16,...
## $ `9/28/20` <dbl> 0, 25, 50, 7, 10, 15, 14, 39, 44, 42, 13, 30, 12, 16,...
## $ `9/29/20` <dbl> 0, 27, 50, 7, 10, 15, 14, 40, 44, 42, 13, 30, 12, 16,...
## $ `9/30/20` <dbl> 0, 27, 52, 7, 10, 15, 15, 40, 44, 42, 13, 30, 12, 16,...
## $ `10/1/20` <dbl> 0, 28, 53, 7, 10, 15, 16, 40, 44, 42, 13, 30, 12, 16,...
## Observations: 811,530
## Variables: 6
## $ countyFIPS <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ countyName <chr> "Statewide Unallocated", "Statewide Unallocated", "State...
## $ state <chr> "AL", "AL", "AL", "AL", "AL", "AL", "AL", "AL", "AL", "A...
## $ stateFIPS <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,...
## $ date <date> 2020-01-22, 2020-01-23, 2020-01-24, 2020-01-25, 2020-01...
## $ cumDeaths <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
# STEP 3: Create appropriately filtered data without creating new clusters
clust_20201003 <- prepClusterCounties(burdenFile=burden_20201003,
maxDate=maxDate_20201003,
minPop=25000,
createClusters=FALSE
)## # A tibble: 1 x 6
## cpm_mean_is0 dpm_mean_is0 cpm_mean_lt100 dpm_mean_lt100 cpm_mean_lt5000
## <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 0 0.0214 0 0.179 0.0761
## # ... with 1 more variable: dpm_mean_lt5000 <dbl># STEP 4: Assess the existing clusters against the new data, including the cumulative plot
helperACC_county_20201003 <- helperAssessCountyClusters(vecCluster=clustVec_county_20200903_new,
dfPop=clust_20201003$countyFiltered,
dfBurden=clust_20201003$countyFiltered,
showCum=TRUE,
thruLabel="Sep 30, 2020",
plotsTogether=TRUE,
orderCluster=TRUE
)
##
## Recency is defined as 2020-09-01 through current
##
## Recency is defined as 2020-09-01 through current## Warning: Removed 1 rows containing missing values (geom_point).
# STEP 5: Add back clusters not used for analysis (code 999) and associated disease data
clusterStateData_20201003 <- helperMakeClusterStateData(helperACC_county_20201003,
dfBurden=clust_20201003$countyDailyPerCapita,
orderCluster=TRUE
)## Joining, by = "fipsCounty"## Joining, by = "fipsCounty"# STEP 6: Run an example state-level summary using several of the largest states
stateCountySummary(states=c("CA", "TX", "FL", "NY", "IL", "PA"),
df=changeOrderLabel(clusterStateData_20201003, grpVars="fipsCounty"),
keyDate=maxDate_20201003,
showQuadrants=TRUE,
showCumulative=TRUE,
facetCumulativeByState = TRUE,
showAllFactorLevels = TRUE
)
# STEP 7: Assess lags for largest counties and counties with highest death rates
# STEP 7a: Save data for top-100 counties by death rate (filtered to only counties of 100k+ population)
top100_dpm_20201003 <- exploreTopCounties(helperACC_county_20201003,
minDate="2020-03-01",
maxDate="2020-09-30",
minPop=100000,
nVar="dpm",
nKey=100,
plotData=FALSE
)##
## *** KEY COUNTIES INCLUDE: ***
## # A tibble: 100 x 5
## state cluster countyName dpm pop
## <chr> <fct> <chr> <dbl> <dbl>
## 1 36005 3 3 - Bronx (NY) 3492. 1418207
## 2 36081 3 3 - Queens (NY) 3218. 2253858
## 3 36047 3 3 - Kings (NY) 2863. 2559903
## 4 34013 3 3 - Essex (NJ) 2663. 798975
## 5 34031 3 3 - Passaic (NJ) 2499. 501826
## 6 34039 3 3 - Union (NJ) 2437. 556341
## 7 36085 3 3 - Richmond (NY) 2281. 476143
## 8 34017 3 3 - Hudson (NJ) 2256. 672391
## 9 34003 3 3 - Bergen (NJ) 2194. 932202
## 10 48061 3 3 - Cameron (TX) 2155. 423163
## # ... with 90 more rows
##
## The best lags are:
## # A tibble: 100 x 5
## countyName state cluster corr lag
## <chr> <chr> <fct> <dbl> <int>
## 1 3 - Hampden (MA) 25013 3 0.924 0
## 2 3 - San Juan (NM) 35045 3 0.836 0
## 3 4 - Baltimore cityMD) 24510 4 0.535 0
## 4 4 - Coconino (AZ) 04005 4 0.265 0
## 5 4 - Hendricks (IN) 18063 4 0.467 0
## 6 4 - Johnson (IN) 18081 4 0.527 0
## 7 4 - Marion (IN) 18097 4 0.544 0
## 8 4 - Montgomery (AL) 01101 4 0.761 0
## 9 4 - Montgomery (MD) 24031 4 0.895 0
## 10 4 - Oakland (MI) 26125 4 0.478 0
## # ... with 90 more rowsglimpse(top100_dpm_20201003)## Observations: 21,200
## Variables: 9
## Groups: countyName, state, cluster [100]
## $ countyName <chr> "1 - Anderson (SC)", "1 - Highlands (FL)", "1 - Webb (TX...
## $ state <chr> "45007", "12055", "48479", "34003", "36005", "48061", "2...
## $ cluster <fct> 1, 1, 1, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,...
## $ date <date> 2020-03-01, 2020-03-01, 2020-03-01, 2020-03-01, 2020-03...
## $ cpm7 <dbl> 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.000000...
## $ dpm7 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ corr <dbl> 0.9199305, 0.7872620, 0.8755114, 0.8252094, 0.9747650, 0...
## $ lag <int> 26, 28, 30, 9, 6, 11, 8, 7, 9, 0, 2, 8, 26, 8, 6, 8, 10,...
## $ cpmlag <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, 0, NA, NA, NA, NA, N...# STEP 7b: Top-3 by population by cluster with plots
exploreTopCounties(helperACC_county_20201003,
minDate="2020-03-01",
maxDate="2020-09-30",
topNBy="cluster",
nVar="pop",
nKey=3,
plotData=TRUE
)##
## *** KEY COUNTIES INCLUDE: ***
## # A tibble: 15 x 5
## state cluster countyName dpm pop
## <chr> <fct> <chr> <dbl> <dbl>
## 1 06037 5 5 - Los Angeles (CA) 655. 10039107
## 2 17031 4 4 - Cook (IL) 1015. 5150233
## 3 48201 1 1 - Harris (TX) 551. 4713325
## 4 04013 4 4 - Maricopa (AZ) 756. 4485414
## 5 06073 2 2 - San Diego (CA) 235. 3338330
## 6 06059 1 1 - Orange (CA) 399. 3175692
## 7 12086 4 4 - Miami-Dade (FL) 1207. 2716940
## 8 48113 1 1 - Dallas (TX) 430. 2635516
## 9 36047 3 3 - Kings (NY) 2863. 2559903
## 10 36081 3 3 - Queens (NY) 3218. 2253858
## 11 53033 5 5 - King (WA) 337. 2252782
## 12 06085 2 2 - Santa Clara (CA) 167. 1927852
## 13 26163 3 3 - Wayne (MI) 1706. 1749343
## 14 06001 2 2 - Alameda (CA) 247. 1671329
## 15 39049 5 5 - Franklin (OH) 467. 1316756
##
## The best lags are:
## # A tibble: 15 x 5
## countyName state cluster corr lag
## <chr> <chr> <fct> <dbl> <int>
## 1 5 - Franklin (OH) 39049 5 0.218 0
## 2 5 - Los Angeles (CA) 06037 5 0.616 0
## 3 2 - San Diego (CA) 06073 2 0.646 2
## 4 5 - King (WA) 53033 5 0.198 2
## 5 4 - Cook (IL) 17031 4 0.836 5
## 6 3 - Kings (NY) 36047 3 0.965 8
## 7 3 - Queens (NY) 36081 3 0.941 8
## 8 1 - Dallas (TX) 48113 1 0.782 9
## 9 3 - Wayne (MI) 26163 3 0.844 10
## 10 1 - Harris (TX) 48201 1 0.740 13
## 11 4 - Maricopa (AZ) 04013 4 0.965 18
## 12 1 - Orange (CA) 06059 1 0.757 24
## 13 2 - Alameda (CA) 06001 2 0.727 24
## 14 2 - Santa Clara (CA) 06085 2 0.487 30
## 15 4 - Miami-Dade (FL) 12086 4 0.785 30## Warning: Removed 26 rows containing missing values (geom_path).## Warning: Removed 26 rows containing missing values (geom_path).
## # A tibble: 3,180 x 9
## # Groups: countyName, state, cluster [15]
## countyName state cluster date cpm7 dpm7 corr lag cpmlag
## <chr> <chr> <fct> <date> <dbl> <dbl> <dbl> <int> <dbl>
## 1 1 - Dallas (TX) 48113 1 2020-03-01 0 0 0.782 9 NA
## 2 1 - Harris (TX) 48201 1 2020-03-01 0 0 0.740 13 NA
## 3 1 - Orange (CA) 06059 1 2020-03-01 0.0900 0 0.757 24 NA
## 4 2 - Alameda (CA) 06001 2 2020-03-01 0.0855 0 0.727 24 NA
## 5 2 - San Diego (CA) 06073 2 2020-03-01 0 0 0.646 2 NA
## 6 2 - Santa Clara (CA) 06085 2 2020-03-01 1.26 0 0.487 30 NA
## 7 3 - Kings (NY) 36047 3 2020-03-01 0 0 0.965 8 NA
## 8 3 - Queens (NY) 36081 3 2020-03-01 0 0 0.941 8 NA
## 9 3 - Wayne (MI) 26163 3 2020-03-01 0 0 0.844 10 NA
## 10 4 - Cook (IL) 17031 4 2020-03-01 0.0555 0 0.836 5 NA
## # ... with 3,170 more rows# STEP 7c: Top-5 by dpm by cluster with plots
exploreTopCounties(helperACC_county_20201003,
minDate="2020-03-01",
maxDate="2020-09-30",
topNBy="cluster",
nVar="dpm",
nKey=5,
plotData=TRUE
)##
## *** KEY COUNTIES INCLUDE: ***
## # A tibble: 25 x 5
## state cluster countyName dpm pop
## <chr> <fct> <chr> <dbl> <dbl>
## 1 36005 3 3 - Bronx (NY) 3492. 1418207
## 2 36081 3 3 - Queens (NY) 3218. 2253858
## 3 36047 3 3 - Kings (NY) 2863. 2559903
## 4 34013 3 3 - Essex (NJ) 2663. 798975
## 5 34031 3 3 - Passaic (NJ) 2499. 501826
## 6 48215 4 4 - Hidalgo (TX) 1755. 868707
## 7 04027 4 4 - Yuma (AZ) 1614. 213787
## 8 22017 4 4 - Caddo (LA) 1503. 240204
## 9 44007 4 4 - Providence (RI) 1398. 638931
## 10 45041 4 4 - Florence (SC) 1381. 138293
## # ... with 15 more rows
##
## The best lags are:
## # A tibble: 25 x 5
## countyName state cluster corr lag
## <chr> <chr> <fct> <dbl> <int>
## 1 2 - Pulaski (AR) 05119 2 0.558 0
## 2 5 - Columbiana (OH) 39029 5 -0.0149 0
## 3 5 - Litchfield (CT) 09005 5 0.922 4
## 4 4 - Caddo (LA) 22017 4 0.210 5
## 5 3 - Bronx (NY) 36005 3 0.975 6
## 6 3 - Essex (NJ) 34013 3 0.876 7
## 7 3 - Kings (NY) 36047 3 0.965 8
## 8 3 - Queens (NY) 36081 3 0.941 8
## 9 4 - Florence (SC) 45041 4 0.856 8
## 10 3 - Passaic (NJ) 34031 3 0.810 9
## # ... with 15 more rows## Warning: Removed 158 rows containing missing values (geom_path).## Warning: Removed 158 rows containing missing values (geom_path).
## # A tibble: 5,300 x 9
## # Groups: countyName, state, cluster [25]
## countyName state cluster date cpm7 dpm7 corr lag cpmlag
## <chr> <chr> <fct> <date> <dbl> <dbl> <dbl> <int> <dbl>
## 1 1 - Anderson (SC) 45007 1 2020-03-01 0 0 0.920 26 NA
## 2 1 - Highlands (FL) 12055 1 2020-03-01 0 0 0.787 28 NA
## 3 1 - Marion (FL) 12083 1 2020-03-01 0 0 0.904 27 NA
## 4 1 - Newton (GA) 13217 1 2020-03-01 0 0 0.781 26 NA
## 5 1 - Webb (TX) 48479 1 2020-03-01 0 0 0.876 30 NA
## 6 2 - Cabarrus (NC) 37025 2 2020-03-01 0 0 0.420 20 NA
## 7 2 - DeKalb (IL) 17037 2 2020-03-01 0 0 0.428 15 NA
## 8 2 - Douglas (NE) 31055 2 2020-03-01 0 0 0.539 18 NA
## 9 2 - Pulaski (AR) 05119 2 2020-03-01 0 0 0.558 0 0
## 10 2 - Warren (KY) 21227 2 2020-03-01 0 0 0.596 16 NA
## # ... with 5,290 more rowsCDC All-Cause Deaths
The CDC maintain public data for all-cause deaths by week and jurisdiction, available at CDC Weekly Deaths by Jurisdiction.
These data are known to have a lag between death and reporting, and the CDC back-correct to report deaths at the time the death occurred even if the death is reported in following weeks. This means totals for recent weeks tend to run low (lag), and the CDC run a projection of the expected total number of deaths given the historical lag times. Per other analysts on the internet, there is currently significant supra-lag, with lag times much longer than historical averages causing CDC projected deaths for recent weeks to be low.
Key Functions (CDC All-Cause Deaths)
Functions for reading and analyzing CDC all-cause deaths data have been sourced above from Coronavirus_Statistics_Functions_v002.R.
Running Key Analyses (CDC All-Cause Deaths)
The CDC data can be loaded and analyzed:
# Read and process the CDC data
cdc20200923 <- readProcessCDC("Weekly_counts_of_deaths_by_jurisdiction_and_age_group_downloaded_20200923.csv",
weekThru=30
)## Observations: 174,311
## Variables: 11
## $ Jurisdiction <chr> "Alabama", "Alabama", "Alabama", "Alabama", "A...
## $ `Week Ending Date` <chr> "1/10/2015", "1/17/2015", "1/24/2015", "1/31/2...
## $ `State Abbreviation` <chr> "AL", "AL", "AL", "AL", "AL", "AL", "AL", "AL"...
## $ Year <int> 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015...
## $ Week <int> 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14,...
## $ `Age Group` <chr> "25-44 years", "25-44 years", "25-44 years", "...
## $ `Number of Deaths` <dbl> 67, 49, 55, 59, 47, 59, 41, 47, 59, 57, 54, 50...
## $ `Time Period` <chr> "2015-2019", "2015-2019", "2015-2019", "2015-2...
## $ Type <chr> "Predicted (weighted)", "Predicted (weighted)"...
## $ Suppress <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA...
## $ Note <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA...
## Observations: 174,311
## Variables: 11
## $ Jurisdiction <chr> "Alabama", "Alabama", "Alabama", "Alabama", "Alabama",...
## $ weekEnding <date> 2015-01-10, 2015-01-17, 2015-01-24, 2015-01-31, 2015-...
## $ state <chr> "AL", "AL", "AL", "AL", "AL", "AL", "AL", "AL", "AL", ...
## $ year <int> 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015, ...
## $ week <int> 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16,...
## $ age <chr> "25-44 years", "25-44 years", "25-44 years", "25-44 ye...
## $ deaths <dbl> 67, 49, 55, 59, 47, 59, 41, 47, 59, 57, 54, 50, 58, 42...
## $ period <chr> "2015-2019", "2015-2019", "2015-2019", "2015-2019", "2...
## $ type <chr> "Predicted (weighted)", "Predicted (weighted)", "Predi...
## $ Suppress <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA...
## $ Note <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA...
##
## Check Control Levels and Record Counts for Renamed Data:
## # A tibble: 6 x 4
## age n n_deaths_na deaths
## <chr> <int> <int> <dbl>
## 1 25-44 years 26066 3 3170502
## 2 45-64 years 31884 7 12464677
## 3 65-74 years 31880 10 12347549
## 4 75-84 years 31902 11 15363924
## 5 85 years and older 31888 9 20067928
## 6 Under 25 years 20691 0 1372225
## # A tibble: 12 x 6
## # Groups: period, year [6]
## period year type n n_deaths_na deaths
## <chr> <int> <chr> <int> <int> <dbl>
## 1 2015-2019 2015 Predicted (weighted) 15285 0 5416393
## 2 2015-2019 2015 Unweighted 15285 0 5416393
## 3 2015-2019 2016 Predicted (weighted) 15365 0 5483764
## 4 2015-2019 2016 Unweighted 15365 0 5483764
## 5 2015-2019 2017 Predicted (weighted) 15318 0 5643350
## 6 2015-2019 2017 Unweighted 15318 0 5643350
## 7 2015-2019 2018 Predicted (weighted) 15305 0 5698002
## 8 2015-2019 2018 Unweighted 15305 0 5698002
## 9 2015-2019 2019 Predicted (weighted) 15319 0 5725516
## 10 2015-2019 2019 Unweighted 15319 0 5725516
## 11 2020 2020 Predicted (weighted) 10586 24 4476340
## 12 2020 2020 Unweighted 10541 16 4376415
## # A tibble: 3 x 5
## # Groups: period [2]
## period Suppress n n_deaths_na deaths
## <chr> <chr> <int> <int> <dbl>
## 1 2015-2019 <NA> 153184 0 5.59e7
## 2 2020 Suppressed (counts highly incomplete, <5~ 40 40 0.
## 3 2020 <NA> 21087 0 8.85e6
## # A tibble: 9 x 5
## # Groups: period [2]
## period Note n n_deaths_na deaths
## <chr> <chr> <int> <int> <dbl>
## 1 2015-20~ <NA> 153184 0 5.59e7
## 2 2020 Data in recent weeks are incomplete. Only~ 16591 0 7.27e6
## 3 2020 Data in recent weeks are incomplete. Only~ 324 0 1.52e5
## 4 2020 Data in recent weeks are incomplete. Only~ 288 30 3.05e4
## 5 2020 Data in recent weeks are incomplete. Only~ 1502 10 4.17e5
## 6 2020 Data in recent weeks are incomplete. Only~ 60 0 2.71e4
## 7 2020 Estimates for Pennsylvania are too low fo~ 48 0 2.23e4
## 8 2020 Weights may be too low to account for und~ 436 0 1.40e5
## 9 2020 <NA> 1878 0 7.97e5
## state Jurisdiction n n_deaths_na deaths
## 1 US United States 3552 0 32276762
## 2 CA California 3552 0 3072016
## 3 FL Florida 3552 0 2357528
## 4 TX Texas 3552 0 2314502
## 5 PA Pennsylvania 3552 0 1548716
## 6 OH Ohio 3552 0 1395014
## 7 IL Illinois 3552 0 1219119
## 8 NY New York 3552 0 1155790
## 9 MI Michigan 3552 0 1111203
## 10 NC North Carolina 3521 17 1051632
## 11 GA Georgia 3551 0 965504
## 12 NJ New Jersey 3546 0 867210
## 13 TN Tennessee 3552 0 840787
## 14 VA Virginia 3552 0 774383
## 15 IN Indiana 3550 0 749760
## 16 MO Missouri 3548 0 728220
## 17 MA Massachusetts 3516 0 685409
## 18 AZ Arizona 3552 0 684537
## 19 YC New York City 3548 0 671106
## 20 WA Washington 3551 0 645406
## 21 AL Alabama 3550 0 598526
## 22 WI Wisconsin 3533 0 592047
## 23 MD Maryland 3546 0 570238
## 24 SC South Carolina 3549 0 560415
## 25 KY Kentucky 3519 0 545032
## 26 LA Louisiana 3545 0 525668
## 27 MN Minnesota 3509 0 503567
## 28 CO Colorado 3550 0 446708
## 29 OK Oklahoma 3541 0 445362
## 30 OR Oregon 3382 0 413553
## 31 MS Mississippi 3488 0 363792
## 32 AR Arkansas 3444 0 361612
## 33 CT Connecticut 3106 13 356416
## 34 IA Iowa 3190 0 339791
## 35 PR Puerto Rico 3272 0 331654
## 36 KS Kansas 3246 0 296520
## 37 NV Nevada 3291 0 289275
## 38 WV West Virginia 3011 10 251046
## 39 UT Utah 3438 0 213931
## 40 NM New Mexico 3140 0 205026
## 41 NE Nebraska 2846 0 188492
## 42 ME Maine 2646 0 160341
## 43 ID Idaho 2766 0 152936
## 44 NH New Hampshire 2672 0 135110
## 45 HI Hawaii 2556 0 124379
## 46 RI Rhode Island 2474 0 114274
## 47 MT Montana 2556 0 109719
## 48 DE Delaware 2558 0 99625
## 49 SD South Dakota 2448 0 86461
## 50 ND North Dakota 2433 0 75079
## 51 DC District of Columbia 2545 0 64202
## 52 VT Vermont 2336 0 61925
## 53 WY Wyoming 2318 0 47230
## 54 AK Alaska 2352 0 42249
## Observations: 174,311
## Variables: 11
## $ Jurisdiction <chr> "Alabama", "Alabama", "Alabama", "Alabama", "Alabama",...
## $ weekEnding <date> 2015-01-10, 2015-01-17, 2015-01-24, 2015-01-31, 2015-...
## $ state <chr> "AL", "AL", "AL", "AL", "AL", "AL", "AL", "AL", "AL", ...
## $ year <fct> 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015, ...
## $ week <int> 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16,...
## $ age <fct> 25-44 years, 25-44 years, 25-44 years, 25-44 years, 25...
## $ deaths <dbl> 67, 49, 55, 59, 47, 59, 41, 47, 59, 57, 54, 50, 58, 42...
## $ period <fct> 2015-2019, 2015-2019, 2015-2019, 2015-2019, 2015-2019,...
## $ type <chr> "Predicted (weighted)", "Predicted (weighted)", "Predi...
## $ Suppress <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA...
## $ Note <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA...
## Observations: 85,466
## Variables: 11
## $ Jurisdiction <chr> "Alabama", "Alabama", "Alabama", "Alabama", "Alabama",...
## $ weekEnding <date> 2015-01-10, 2015-01-17, 2015-01-24, 2015-01-31, 2015-...
## $ state <chr> "AL", "AL", "AL", "AL", "AL", "AL", "AL", "AL", "AL", ...
## $ year <fct> 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015, ...
## $ week <int> 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16,...
## $ age <fct> 25-44 years, 25-44 years, 25-44 years, 25-44 years, 25...
## $ deaths <dbl> 67, 49, 55, 59, 47, 59, 41, 47, 59, 57, 54, 50, 58, 42...
## $ period <fct> 2015-2019, 2015-2019, 2015-2019, 2015-2019, 2015-2019,...
## $ type <chr> "Predicted (weighted)", "Predicted (weighted)", "Predi...
## $ Suppress <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA...
## $ Note <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA...
##
## Confirm that data suppressions and NA deaths have been aliminated:
## # A tibble: 0 x 11
## # ... with 11 variables: Jurisdiction <chr>, weekEnding <date>, state <chr>,
## # year <fct>, week <int>, age <fct>, deaths <dbl>, period <fct>, type <chr>,
## # Suppress <chr>, Note <chr>
## Observations: 80,379
## Variables: 12
## $ Jurisdiction <chr> "Alabama", "Alabama", "Alabama", "Alabama", "Alabama",...
## $ weekEnding <date> 2015-01-10, 2015-01-10, 2015-01-10, 2015-01-10, 2015-...
## $ state <chr> "AL", "AL", "AL", "AL", "AL", "AL", "AL", "AL", "AL", ...
## $ year <fct> 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015, ...
## $ week <int> 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, ...
## $ age <fct> Under 25 years, 25-44 years, 45-64 years, 65-74 years,...
## $ period <fct> 2015-2019, 2015-2019, 2015-2019, 2015-2019, 2015-2019,...
## $ type <chr> "Predicted (weighted)", "Predicted (weighted)", "Predi...
## $ Suppress <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA...
## $ n <int> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...
## $ deaths <dbl> 25, 67, 253, 202, 272, 320, 28, 49, 256, 222, 253, 332...
## $ Note <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA...
##
## First duplicate is in row number (0 means no duplicates): 0# Generate plots of the processed CDC data
cdcBasicPlots(cdc20200923, clustVec=test_hier5$useClusters)
# Example cohort analysis for age 65+
list_65plus <- cdcCohortAnalysis(cohortName="65+ years old",
df=cdc20200923,
critFilter=list("age"=c("65-74 years",
"75-84 years",
"85 years and older"
)
),
curYear=2020,
startYear=2015,
startWeek=9,
plotTitle="All-cause deaths for 65+ years old cohort",
predActualPlotsOnePage=TRUE
)
# Example cohort analysis for full US
list_allUS <- cdcCohortAnalysis(cohortName="all ages, all states",
df=cdc20200923,
curYear=2020,
startYear=2015,
startWeek=9,
plotTitle="All-cause US total deaths",
predActualPlotsOnePage=TRUE
)
# Example cohort analysis for handful of state hit early
list_early <- cdcCohortAnalysis(cohortName="all ages, NY/NJ/CT/MA",
df=cdc20200923,
critFilter=list("state"=c("NY", "NJ", "CT", "MA")),
curYear=2020,
startYear=2015,
startWeek=9,
plotTitle="All-cause total deaths for NY/NJ/CT/MA",
predActualPlotsOnePage=TRUE
)
Next steps are to continue with the CDC aggregates function (analyze by cluster or age or state ot etc.):
# Testing the aggregation function for cluster
# Need to fix plot chart titles
clusterList_hier5_201001 <- helperKeyStateClusterMetrics(test_hier5_201001)
clusterAgg_20200923 <- cdcAggregateSummary(df=cdc20200923,
critVar="state",
critSubsets=clusterList_hier5_201001$stateCluster,
startWeek=9,
critListNames=paste0("cluster ", 1:5),
factorCritList=FALSE,
popData=clusterList_hier5_201001$pop,
cvDeathData=clusterList_hier5_201001$deaths,
idVarName="cluster"
)
# Testing the aggregation function for state (no plots)
stateAgg_20200923 <- cdcAggregateSummary(df=cdc20200923,
critVar="state",
critSubsets=names(clusterList_hier5_201001$clData),
startWeek=9,
idVarName="state",
subListNames=names(clusterList_hier5_201001$clData),
showAllPlots=FALSE
)
# Create a mapping of epiweek to month (use 2020 for this)
epiMonth <- tibble::tibble(dt=as.Date("2020-01-01")+0:365,
month=lubridate::month(dt),
quarter=lubridate::quarter(dt),
ew=lubridate::epiweek(dt)
) %>%
count(ew, month, quarter) %>%
arrange(ew, -n) %>%
group_by(ew) %>%
summarize(month=factor(month.abb[first(month)], levels=month.abb), quarter=first(quarter))
# Create plots by state
helperKeyStateExcessPlots(df=stateAgg_20200923,
epiMonth=epiMonth,
cvDeaths=test_hier5_201001$consolidatedPlotData,
startWeek=10,
cvDeathDate=as.Date("2020-07-31"),
subT="CDC data through July 2020 (Q3 incomplete)"
)## Warning: Column `state` joining factor and character vector, coercing into
## character vector
## Joining, by = "state"
## Warning: Removed 1 rows containing missing values (position_stack).## Warning: Removed 1 rows containing missing values (geom_text).
## # A tibble: 357 x 7
## state quarter month postStart excess pop excesspm
## <chr> <fct> <fct> <dbl> <dbl> <dbl> <dbl>
## 1 AK Q1-2020 Jan 0 16.3 738432 22.1
## 2 AK Q1-2020 Feb 0 9.85 738432 13.3
## 3 AK Q1-2020 Mar 1 -21.6 738432 -29.2
## 4 AK Q2-2020 Apr 1 -9.29 738432 -12.6
## 5 AK Q2-2020 May 1 28.0 738432 38.0
## 6 AK Q2-2020 Jun 1 16.4 738432 22.3
## 7 AK Q3-2020 Jul 1 5.85 738432 7.92
## 8 AL Q1-2020 Jan 0 -527. 4858979 -108.
## 9 AL Q1-2020 Feb 0 -84.7 4858979 -17.4
## 10 AL Q1-2020 Mar 1 -74.5 4858979 -15.3
## # ... with 347 more rows# Testing the aggregation function for age (no plots)
ageAgg_20200923 <- cdcAggregateSummary(df=cdc20200923,
critVar="age",
critSubsets=levels(cdc20200923$age),
startWeek=9,
idVarName="age",
subListNames=levels(cdc20200923$age),
showAllPlots=TRUE
)
# Estimated US population by age (2020)
usPopAge2020 <- survival::uspop2[, , "2020"] %>%
apply(1, FUN=sum) %>%
tibble::tibble(ageActual=as.integer(names(.)), pop_2020=.)
usPopAge2020## # A tibble: 101 x 2
## ageActual pop_2020
## <int> <int>
## 1 0 4583634
## 2 1 4575533
## 3 2 4567477
## 4 3 4561292
## 5 4 4557600
## 6 5 4555265
## 7 6 4552366
## 8 7 4548108
## 9 8 4542047
## 10 9 4534181
## # ... with 91 more rows# Sums by age bucket
usPopBucket2020 <- usPopAge2020 %>%
mutate(age=factor(case_when(ageActual <= 24 ~ "Under 25 years",
ageActual <= 44 ~ "25-44 years",
ageActual <= 64 ~ "45-64 years",
ageActual <= 74 ~ "65-74 years",
ageActual <= 84 ~ "75-84 years",
TRUE ~ "85 years and older"
), levels=levels(cdc20200923$age)
)
) %>%
group_by(age) %>%
summarize(pop=sum(pop_2020))
usPopBucket2020## # A tibble: 6 x 2
## age pop
## <fct> <int>
## 1 Under 25 years 112501697
## 2 25-44 years 89724301
## 3 45-64 years 84356197
## 4 65-74 years 32312186
## 5 75-84 years 15895265
## 6 85 years and older 6597019# Create plots by age
helperKeyAgeExcessPlots(df=ageAgg_20200923,
epiMonth=epiMonth,
cvDeaths=test_hier5_201001$consolidatedPlotData,
popData=usPopBucket2020,
startWeek=10,
cvDeathDate=as.Date("2020-07-31"),
subT="CDC data through July 2020 (Q3 incomplete)"
)
## # A tibble: 42 x 7
## age quarter month postStart excess pop excesspm
## <fct> <fct> <fct> <dbl> <dbl> <int> <dbl>
## 1 Under 25 years Q1-2020 Jan 0 249. 112501697 2.21
## 2 Under 25 years Q1-2020 Feb 0 113. 112501697 1.00
## 3 Under 25 years Q1-2020 Mar 1 -72.4 112501697 -0.644
## 4 Under 25 years Q2-2020 Apr 1 154. 112501697 1.37
## 5 Under 25 years Q2-2020 May 1 216. 112501697 1.92
## 6 Under 25 years Q2-2020 Jun 1 468. 112501697 4.16
## 7 Under 25 years Q3-2020 Jul 1 519. 112501697 4.61
## 8 25-44 years Q1-2020 Jan 0 337. 89724301 3.76
## 9 25-44 years Q1-2020 Feb 0 236. 89724301 2.63
## 10 25-44 years Q1-2020 Mar 1 851. 89724301 9.48
## # ... with 32 more rows# Create data at the level of elderly and non-elderly, using 65 as the start if elderly
ageOld <- c("65-74 years", "75-84 years", "85 years and older")
cdcTwoAge <- cdc20200923 %>%
mutate(ageSplit=factor(ifelse(age %in% ageOld, "65 and over", "Under 65"),
levels=c("Under 65", "65 and over")
)
) %>%
group_by(state, year, week, ageSplit) %>%
summarize(deaths=sum(deaths)) %>%
ungroup() %>%
mutate(keyVar=paste(ageSplit, state, sep="_"))
cdcTwoAge## # A tibble: 29,552 x 6
## state year week ageSplit deaths keyVar
## <chr> <fct> <int> <fct> <dbl> <chr>
## 1 AK 2015 1 Under 65 22 Under 65_AK
## 2 AK 2015 1 65 and over 40 65 and over_AK
## 3 AK 2015 2 Under 65 22 Under 65_AK
## 4 AK 2015 2 65 and over 55 65 and over_AK
## 5 AK 2015 3 Under 65 19 Under 65_AK
## 6 AK 2015 3 65 and over 53 65 and over_AK
## 7 AK 2015 4 Under 65 17 Under 65_AK
## 8 AK 2015 4 65 and over 51 65 and over_AK
## 9 AK 2015 5 Under 65 41 Under 65_AK
## 10 AK 2015 5 65 and over 33 65 and over_AK
## # ... with 29,542 more rows# Testing the aggregation function for state and elderly (no plots)
ageStateAgg_20200923 <- cdcAggregateSummary(df=cdcTwoAge,
critVar="keyVar",
critSubsets=unique(cdcTwoAge$keyVar),
startWeek=9,
idVarName="keyVar",
subListNames=unique(cdcTwoAge$keyVar),
showAllPlots=FALSE
) %>%
tidyr::separate(keyVar, into=c("age", "state"), sep="_")
# Plot of excess deaths ratio by state and age
ageStateAgg_20200923 %>%
filter(year==2020, week>=10) %>%
group_by(age, state) %>%
summarize(excess=sum(delta)) %>%
ggplot(aes(x=fct_reorder(state, excess, .fun=sum), y=excess/1000, fill=age)) +
geom_col(position="fill") +
coord_flip() +
labs(x="State",
y="% of All-cause excess deaths",
title="Proportion of all-cause excess deaths by age cohort in 2020",
subtitle="CDC data March 2020 through July 2020"
) +
scale_fill_discrete("Age Group") +
geom_hline(aes(yintercept=0.25), lty=2)
# Plots by age cohort
for (age in levels(cdcTwoAge$ageSplit)) {
cdcCohortAnalysis(df=cdcTwoAge,
cohortName=paste0("ages ", age),
critFilter=list("ageSplit"=age),
plotTitle=paste0("All-cause total deaths for age ", age),
showSubsetPlots=FALSE,
showPredActualPlots=TRUE,
predActualPlotsOnePage=TRUE
)
}
Next steps are to further clean up and integrate the functions so that the full process can be run with more recent data using fewer changes to the code.
Running the Full Process
First, data are downloaded from COVID Tracking Project (run only once) without creating clusters (this chunk is set to eval=FALSE to avoid over-writing previously downloaded data):
# Test function for hierarchical clustering with Vermont reassigned to New Hampshire
locDownload <- "./RInputFiles/Coronavirus/CV_downloaded_201010.csv"
test_hier5_201010 <- readRunCOVIDTrackingProject(thruLabel="Oct 9, 2020",
downloadTo=locDownload,
compareFile=test_hier5_201001$dfRaw,
useClusters=test_hier5_201001$useClusters
)The segments are recreated from the downloaded data so that evolution in segment type and membership can be explored:
# Explore the newly downloaded data and the dendrogram if using for clusters
locDownload <- "./RInputFiles/Coronavirus/CV_downloaded_201010.csv"
test_hier_201010_dendonly <- readRunCOVIDTrackingProject(thruLabel="Oct 9, 2020",
readFrom=locDownload,
compareFile=test_hier5_201001$dfRaw,
hierarchical=TRUE,
minShape=3,
ratioDeathvsCase = 5,
ratioTotalvsShape = 0.5,
minDeath=100,
minCase=10000,
skipAssessmentPlots=TRUE
)## Parsed with column specification:
## cols(
## .default = col_double(),
## state = col_character(),
## dataQualityGrade = col_character(),
## lastUpdateEt = col_character(),
## dateModified = col_datetime(format = ""),
## checkTimeEt = col_character(),
## dateChecked = col_datetime(format = ""),
## fips = col_character(),
## totalTestResultsSource = col_character(),
## hash = col_character(),
## grade = col_logical()
## )## See spec(...) for full column specifications.##
## File is unique by state and date
##
##
## Overall control totals in file:
## # A tibble: 1 x 3
## positiveIncrease deathIncrease hospitalizedCurrently
## <dbl> <dbl> <dbl>
## 1 7623536 205470 8114969
##
## *** COMPARISONS TO REFERENCE FILE: compareFile
##
## Checkin for similarity of: column names
## In reference but not in current:
## In current but not in reference: probableCases
##
## Checkin for similarity of: states
## In reference but not in current:
## In current but not in reference:
##
## Checkin for similarity of: dates
## In reference but not in current:
## In current but not in reference: 2020-10-09 2020-10-08 2020-10-07 2020-10-06 2020-10-05 2020-10-04 2020-10-03 2020-10-02 2020-10-01
##
## *** Difference of at least 5 and difference is at least 1%:## Joining, by = c("date", "name")## [1] date name newValue oldValue
## <0 rows> (or 0-length row.names)## Joining, by = c("date", "name")## Warning: Removed 9 rows containing missing values (geom_path).##
##
## *** Difference of at least 5 and difference is at least 1%:## Joining, by = c("state", "name")
## state name newValue oldValue
## 1 HI positiveIncrease 12469 12289
## Observations: 12,309
## Variables: 55
## $ date <date> 2020-10-09, 2020-10-09, 2020-10-09, 20...
## $ state <chr> "AK", "AL", "AR", "AS", "AZ", "CA", "CO...
## $ positive <dbl> 10176, 163465, 90145, 0, 224084, 838606...
## $ probableCases <dbl> NA, 19565, 4165, NA, 4837, NA, 5226, 24...
## $ negative <dbl> 482894, 1052552, 1025546, 1616, 1324198...
## $ pending <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,...
## $ totalTestResults <dbl> 493070, 1196452, 1111526, 1616, 1543445...
## $ hospitalizedCurrently <dbl> 51, 816, 546, NA, 706, 3186, 356, 134, ...
## $ hospitalizedCumulative <dbl> NA, 17989, 5805, NA, 20199, NA, 7834, 1...
## $ inIcuCurrently <dbl> NA, NA, 237, NA, 155, 742, NA, NA, 27, ...
## $ inIcuCumulative <dbl> NA, 1884, NA, NA, NA, NA, NA, NA, NA, N...
## $ onVentilatorCurrently <dbl> 6, NA, 102, NA, 70, NA, NA, NA, 13, NA,...
## $ onVentilatorCumulative <dbl> NA, 1063, 723, NA, NA, NA, NA, NA, NA, ...
## $ recovered <dbl> 5734, 71240, 81563, NA, 36723, NA, 6756...
## $ dataQualityGrade <chr> "A", "A", "A+", "D", "A+", "B", "A", "B...
## $ lastUpdateEt <chr> "10/9/2020 03:59", "10/9/2020 11:00", "...
## $ dateModified <dttm> 2020-10-09 03:59:00, 2020-10-09 11:00:...
## $ checkTimeEt <chr> "10/08 23:59", "10/09 07:00", "10/07 20...
## $ death <dbl> 60, 2653, 1503, 0, 5746, 16428, 1997, 4...
## $ hospitalized <dbl> NA, 17989, 5805, NA, 20199, NA, 7834, 1...
## $ dateChecked <dttm> 2020-10-09 03:59:00, 2020-10-09 11:00:...
## $ totalTestsViral <dbl> 493070, 1196452, 1111526, 1616, NA, 157...
## $ positiveTestsViral <dbl> 9256, NA, NA, NA, NA, NA, NA, NA, NA, N...
## $ negativeTestsViral <dbl> 483521, NA, 1025546, NA, NA, NA, NA, NA...
## $ positiveCasesViral <dbl> 10176, 143900, 85980, 0, 219247, 838606...
## $ deathConfirmed <dbl> 60, 2496, 1359, NA, 5460, NA, NA, 3631,...
## $ deathProbable <dbl> NA, 157, 144, NA, 286, NA, NA, 899, NA,...
## $ totalTestEncountersViral <dbl> NA, NA, NA, NA, NA, NA, 1496198, NA, 42...
## $ totalTestsPeopleViral <dbl> NA, NA, NA, NA, 1543445, NA, 984996, NA...
## $ totalTestsAntibody <dbl> NA, NA, NA, NA, 300333, NA, 172162, NA,...
## $ positiveTestsAntibody <dbl> NA, NA, NA, NA, NA, NA, 12116, NA, NA, ...
## $ negativeTestsAntibody <dbl> NA, NA, NA, NA, NA, NA, 160046, NA, NA,...
## $ totalTestsPeopleAntibody <dbl> NA, 59957, NA, NA, NA, NA, NA, NA, NA, ...
## $ positiveTestsPeopleAntibody <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,...
## $ negativeTestsPeopleAntibody <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,...
## $ totalTestsPeopleAntigen <dbl> NA, NA, 25538, NA, NA, NA, NA, NA, NA, ...
## $ positiveTestsPeopleAntigen <dbl> NA, NA, 4591, NA, NA, NA, NA, NA, NA, N...
## $ totalTestsAntigen <dbl> NA, NA, 21856, NA, NA, NA, NA, NA, NA, ...
## $ positiveTestsAntigen <dbl> NA, NA, 3300, NA, NA, NA, NA, NA, NA, N...
## $ fips <chr> "02", "01", "05", "60", "04", "06", "08...
## $ positiveIncrease <dbl> 180, 1490, 0, 0, 683, 3806, 863, 290, 7...
## $ negativeIncrease <dbl> 1719, 10506, 0, 0, 11699, 109068, 9322,...
## $ total <dbl> 493070, 1216017, 1115691, 1616, 1548282...
## $ totalTestResultsSource <chr> "posNeg", "posNeg", "posNeg", "posNeg",...
## $ totalTestResultsIncrease <dbl> 1899, 11742, 0, 0, 12350, 112874, 18897...
## $ posNeg <dbl> 493070, 1216017, 1115691, 1616, 1548282...
## $ deathIncrease <dbl> 0, 16, 0, 0, 3, 67, 7, 3, 0, 0, 118, 54...
## $ hospitalizedIncrease <dbl> 0, 0, 0, 0, 28, 0, 63, 0, 0, 0, 196, 12...
## $ hash <chr> "3c160d82e90df263b41bbb98bcc4d2f6d17ff9...
## $ commercialScore <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...
## $ negativeRegularScore <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...
## $ negativeScore <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...
## $ positiveScore <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...
## $ score <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...
## $ grade <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,...
##
##
## Control totals - note that validState other than TRUE will be discarded
##
## # A tibble: 2 x 6
## validState cases deaths hosp tests n
## <lgl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 FALSE 57271 800 NA 451888 1040
## 2 TRUE 7566265 204670 NA 112888209 11269
## Observations: 11,269
## Variables: 6
## $ date <date> 2020-10-09, 2020-10-09, 2020-10-09, 2020-10-09, 2020-10-09,...
## $ state <chr> "AK", "AL", "AR", "AZ", "CA", "CO", "CT", "DC", "DE", "FL", ...
## $ cases <dbl> 180, 1490, 0, 683, 3806, 863, 290, 78, 145, 2908, 1625, 101,...
## $ deaths <dbl> 0, 16, 0, 3, 67, 7, 3, 0, 0, 118, 54, 1, 16, 3, 32, 19, 40, ...
## $ hosp <dbl> 51, 816, 546, 706, 3186, 356, 134, 99, 104, 2143, 1717, 111,...
## $ tests <dbl> 1899, 11742, 0, 12350, 112874, 18897, 17281, 5409, 2865, 284...
## Observations: 11,269
## Variables: 14
## $ date <date> 2020-01-22, 2020-01-22, 2020-01-23, 2020-01-23, 2020-01-24,...
## $ state <chr> "MA", "WA", "MA", "WA", "MA", "WA", "MA", "WA", "MA", "WA", ...
## $ cases <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...
## $ deaths <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...
## $ hosp <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, ...
## $ tests <dbl> 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, ...
## $ cpm <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...
## $ dpm <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...
## $ hpm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, ...
## $ tpm <dbl> 0.0000000, 0.0000000, 0.1471796, 0.0000000, 0.0000000, 0.000...
## $ cpm7 <dbl> NA, NA, NA, NA, NA, NA, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...
## $ dpm7 <dbl> NA, NA, NA, NA, NA, NA, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...
## $ hpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, ...
## $ tpm7 <dbl> NA, NA, NA, NA, NA, NA, 0.04205130, 0.00000000, 0.06307695, ...
The dendrogram reveals similarities in several of the main clusters - a clear early and high group (NJ, NY, CT, MA); a clear late and high group (GA, FL, SC, TX, AL, NV, AZ, MS); a clear early and medium group (MI, DE, PA, IN, IL, IN, MD); 1-2 groups that have had low death rates so far; and two groups (LA, DC, RI) and (VT, ME, CO, WA) that appear different than in previous analyses.
Cutting to seven segments should allow for further exploration of the two stand-alone groups of distinct states while leaving most other states in their previous segments. The full process is then run:
locDownload <- "./RInputFiles/Coronavirus/CV_downloaded_201010.csv"
test_hier7_201010 <- readRunCOVIDTrackingProject(thruLabel="Oct 9, 2020",
readFrom=locDownload,
compareFile=test_hier5_201001$dfRaw,
hierarchical=TRUE,
kCut=7,
minShape=3,
ratioDeathvsCase = 5,
ratioTotalvsShape = 0.5,
minDeath=100,
minCase=10000
)## Parsed with column specification:
## cols(
## .default = col_double(),
## state = col_character(),
## dataQualityGrade = col_character(),
## lastUpdateEt = col_character(),
## dateModified = col_datetime(format = ""),
## checkTimeEt = col_character(),
## dateChecked = col_datetime(format = ""),
## fips = col_character(),
## totalTestResultsSource = col_character(),
## hash = col_character(),
## grade = col_logical()
## )## See spec(...) for full column specifications.##
## File is unique by state and date
##
##
## Overall control totals in file:
## # A tibble: 1 x 3
## positiveIncrease deathIncrease hospitalizedCurrently
## <dbl> <dbl> <dbl>
## 1 7623536 205470 8114969
##
## *** COMPARISONS TO REFERENCE FILE: compareFile
##
## Checkin for similarity of: column names
## In reference but not in current:
## In current but not in reference: probableCases
##
## Checkin for similarity of: states
## In reference but not in current:
## In current but not in reference:
##
## Checkin for similarity of: dates
## In reference but not in current:
## In current but not in reference: 2020-10-09 2020-10-08 2020-10-07 2020-10-06 2020-10-05 2020-10-04 2020-10-03 2020-10-02 2020-10-01
##
## *** Difference of at least 5 and difference is at least 1%:## Joining, by = c("date", "name")## [1] date name newValue oldValue
## <0 rows> (or 0-length row.names)## Joining, by = c("date", "name")## Warning: Removed 9 rows containing missing values (geom_path).##
##
## *** Difference of at least 5 and difference is at least 1%:## Joining, by = c("state", "name")
## state name newValue oldValue
## 1 HI positiveIncrease 12469 12289
## Observations: 12,309
## Variables: 55
## $ date <date> 2020-10-09, 2020-10-09, 2020-10-09, 20...
## $ state <chr> "AK", "AL", "AR", "AS", "AZ", "CA", "CO...
## $ positive <dbl> 10176, 163465, 90145, 0, 224084, 838606...
## $ probableCases <dbl> NA, 19565, 4165, NA, 4837, NA, 5226, 24...
## $ negative <dbl> 482894, 1052552, 1025546, 1616, 1324198...
## $ pending <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,...
## $ totalTestResults <dbl> 493070, 1196452, 1111526, 1616, 1543445...
## $ hospitalizedCurrently <dbl> 51, 816, 546, NA, 706, 3186, 356, 134, ...
## $ hospitalizedCumulative <dbl> NA, 17989, 5805, NA, 20199, NA, 7834, 1...
## $ inIcuCurrently <dbl> NA, NA, 237, NA, 155, 742, NA, NA, 27, ...
## $ inIcuCumulative <dbl> NA, 1884, NA, NA, NA, NA, NA, NA, NA, N...
## $ onVentilatorCurrently <dbl> 6, NA, 102, NA, 70, NA, NA, NA, 13, NA,...
## $ onVentilatorCumulative <dbl> NA, 1063, 723, NA, NA, NA, NA, NA, NA, ...
## $ recovered <dbl> 5734, 71240, 81563, NA, 36723, NA, 6756...
## $ dataQualityGrade <chr> "A", "A", "A+", "D", "A+", "B", "A", "B...
## $ lastUpdateEt <chr> "10/9/2020 03:59", "10/9/2020 11:00", "...
## $ dateModified <dttm> 2020-10-09 03:59:00, 2020-10-09 11:00:...
## $ checkTimeEt <chr> "10/08 23:59", "10/09 07:00", "10/07 20...
## $ death <dbl> 60, 2653, 1503, 0, 5746, 16428, 1997, 4...
## $ hospitalized <dbl> NA, 17989, 5805, NA, 20199, NA, 7834, 1...
## $ dateChecked <dttm> 2020-10-09 03:59:00, 2020-10-09 11:00:...
## $ totalTestsViral <dbl> 493070, 1196452, 1111526, 1616, NA, 157...
## $ positiveTestsViral <dbl> 9256, NA, NA, NA, NA, NA, NA, NA, NA, N...
## $ negativeTestsViral <dbl> 483521, NA, 1025546, NA, NA, NA, NA, NA...
## $ positiveCasesViral <dbl> 10176, 143900, 85980, 0, 219247, 838606...
## $ deathConfirmed <dbl> 60, 2496, 1359, NA, 5460, NA, NA, 3631,...
## $ deathProbable <dbl> NA, 157, 144, NA, 286, NA, NA, 899, NA,...
## $ totalTestEncountersViral <dbl> NA, NA, NA, NA, NA, NA, 1496198, NA, 42...
## $ totalTestsPeopleViral <dbl> NA, NA, NA, NA, 1543445, NA, 984996, NA...
## $ totalTestsAntibody <dbl> NA, NA, NA, NA, 300333, NA, 172162, NA,...
## $ positiveTestsAntibody <dbl> NA, NA, NA, NA, NA, NA, 12116, NA, NA, ...
## $ negativeTestsAntibody <dbl> NA, NA, NA, NA, NA, NA, 160046, NA, NA,...
## $ totalTestsPeopleAntibody <dbl> NA, 59957, NA, NA, NA, NA, NA, NA, NA, ...
## $ positiveTestsPeopleAntibody <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,...
## $ negativeTestsPeopleAntibody <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,...
## $ totalTestsPeopleAntigen <dbl> NA, NA, 25538, NA, NA, NA, NA, NA, NA, ...
## $ positiveTestsPeopleAntigen <dbl> NA, NA, 4591, NA, NA, NA, NA, NA, NA, N...
## $ totalTestsAntigen <dbl> NA, NA, 21856, NA, NA, NA, NA, NA, NA, ...
## $ positiveTestsAntigen <dbl> NA, NA, 3300, NA, NA, NA, NA, NA, NA, N...
## $ fips <chr> "02", "01", "05", "60", "04", "06", "08...
## $ positiveIncrease <dbl> 180, 1490, 0, 0, 683, 3806, 863, 290, 7...
## $ negativeIncrease <dbl> 1719, 10506, 0, 0, 11699, 109068, 9322,...
## $ total <dbl> 493070, 1216017, 1115691, 1616, 1548282...
## $ totalTestResultsSource <chr> "posNeg", "posNeg", "posNeg", "posNeg",...
## $ totalTestResultsIncrease <dbl> 1899, 11742, 0, 0, 12350, 112874, 18897...
## $ posNeg <dbl> 493070, 1216017, 1115691, 1616, 1548282...
## $ deathIncrease <dbl> 0, 16, 0, 0, 3, 67, 7, 3, 0, 0, 118, 54...
## $ hospitalizedIncrease <dbl> 0, 0, 0, 0, 28, 0, 63, 0, 0, 0, 196, 12...
## $ hash <chr> "3c160d82e90df263b41bbb98bcc4d2f6d17ff9...
## $ commercialScore <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...
## $ negativeRegularScore <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...
## $ negativeScore <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...
## $ positiveScore <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...
## $ score <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...
## $ grade <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,...
##
##
## Control totals - note that validState other than TRUE will be discarded
##
## # A tibble: 2 x 6
## validState cases deaths hosp tests n
## <lgl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 FALSE 57271 800 NA 451888 1040
## 2 TRUE 7566265 204670 NA 112888209 11269
## Observations: 11,269
## Variables: 6
## $ date <date> 2020-10-09, 2020-10-09, 2020-10-09, 2020-10-09, 2020-10-09,...
## $ state <chr> "AK", "AL", "AR", "AZ", "CA", "CO", "CT", "DC", "DE", "FL", ...
## $ cases <dbl> 180, 1490, 0, 683, 3806, 863, 290, 78, 145, 2908, 1625, 101,...
## $ deaths <dbl> 0, 16, 0, 3, 67, 7, 3, 0, 0, 118, 54, 1, 16, 3, 32, 19, 40, ...
## $ hosp <dbl> 51, 816, 546, 706, 3186, 356, 134, 99, 104, 2143, 1717, 111,...
## $ tests <dbl> 1899, 11742, 0, 12350, 112874, 18897, 17281, 5409, 2865, 284...
## Observations: 11,269
## Variables: 14
## $ date <date> 2020-01-22, 2020-01-22, 2020-01-23, 2020-01-23, 2020-01-24,...
## $ state <chr> "MA", "WA", "MA", "WA", "MA", "WA", "MA", "WA", "MA", "WA", ...
## $ cases <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...
## $ deaths <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...
## $ hosp <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, ...
## $ tests <dbl> 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, ...
## $ cpm <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...
## $ dpm <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...
## $ hpm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, ...
## $ tpm <dbl> 0.0000000, 0.0000000, 0.1471796, 0.0000000, 0.0000000, 0.000...
## $ cpm7 <dbl> NA, NA, NA, NA, NA, NA, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...
## $ dpm7 <dbl> NA, NA, NA, NA, NA, NA, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...
## $ hpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, ...
## $ tpm7 <dbl> NA, NA, NA, NA, NA, NA, 0.04205130, 0.00000000, 0.06307695, ...
##
## Recency is defined as 2020-09-10 through current
##
## Recency is defined as 2020-09-10 through current
The LA, DC, RI segment does not merit being on its own. It has a small population (6 million) and has broadly the same shape, with somewhat higher deaths, as the much larger MI, DE, PA, IN, IL, MD segment. The number of segments can be cut back to 6 as this is the first segment that would be consolidated by that.
The VT, ME, CO, WA segment also questionably merit stand-alone treatment. It is a variation of the segment that had mild disease early, but is rather differentiated on the dendrogram and has a population (15 million) that is at least in the same order of magnitude as the other clusters.
Suppose that 6 segments are created, using the parameters “as is”:
locDownload <- "./RInputFiles/Coronavirus/CV_downloaded_201010.csv"
test_hier6_201010 <- readRunCOVIDTrackingProject(thruLabel="Oct 9, 2020",
readFrom=locDownload,
compareFile=test_hier5_201001$dfRaw,
hierarchical=TRUE,
kCut=6,
minShape=3,
ratioDeathvsCase = 5,
ratioTotalvsShape = 0.5,
minDeath=100,
minCase=10000
)## Parsed with column specification:
## cols(
## .default = col_double(),
## state = col_character(),
## dataQualityGrade = col_character(),
## lastUpdateEt = col_character(),
## dateModified = col_datetime(format = ""),
## checkTimeEt = col_character(),
## dateChecked = col_datetime(format = ""),
## fips = col_character(),
## totalTestResultsSource = col_character(),
## hash = col_character(),
## grade = col_logical()
## )## See spec(...) for full column specifications.##
## File is unique by state and date
##
##
## Overall control totals in file:
## # A tibble: 1 x 3
## positiveIncrease deathIncrease hospitalizedCurrently
## <dbl> <dbl> <dbl>
## 1 7623536 205470 8114969
##
## *** COMPARISONS TO REFERENCE FILE: compareFile
##
## Checkin for similarity of: column names
## In reference but not in current:
## In current but not in reference: probableCases
##
## Checkin for similarity of: states
## In reference but not in current:
## In current but not in reference:
##
## Checkin for similarity of: dates
## In reference but not in current:
## In current but not in reference: 2020-10-09 2020-10-08 2020-10-07 2020-10-06 2020-10-05 2020-10-04 2020-10-03 2020-10-02 2020-10-01
##
## *** Difference of at least 5 and difference is at least 1%:## Joining, by = c("date", "name")## [1] date name newValue oldValue
## <0 rows> (or 0-length row.names)## Joining, by = c("date", "name")## Warning: Removed 9 rows containing missing values (geom_path).##
##
## *** Difference of at least 5 and difference is at least 1%:## Joining, by = c("state", "name")
## state name newValue oldValue
## 1 HI positiveIncrease 12469 12289
## Observations: 12,309
## Variables: 55
## $ date <date> 2020-10-09, 2020-10-09, 2020-10-09, 20...
## $ state <chr> "AK", "AL", "AR", "AS", "AZ", "CA", "CO...
## $ positive <dbl> 10176, 163465, 90145, 0, 224084, 838606...
## $ probableCases <dbl> NA, 19565, 4165, NA, 4837, NA, 5226, 24...
## $ negative <dbl> 482894, 1052552, 1025546, 1616, 1324198...
## $ pending <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,...
## $ totalTestResults <dbl> 493070, 1196452, 1111526, 1616, 1543445...
## $ hospitalizedCurrently <dbl> 51, 816, 546, NA, 706, 3186, 356, 134, ...
## $ hospitalizedCumulative <dbl> NA, 17989, 5805, NA, 20199, NA, 7834, 1...
## $ inIcuCurrently <dbl> NA, NA, 237, NA, 155, 742, NA, NA, 27, ...
## $ inIcuCumulative <dbl> NA, 1884, NA, NA, NA, NA, NA, NA, NA, N...
## $ onVentilatorCurrently <dbl> 6, NA, 102, NA, 70, NA, NA, NA, 13, NA,...
## $ onVentilatorCumulative <dbl> NA, 1063, 723, NA, NA, NA, NA, NA, NA, ...
## $ recovered <dbl> 5734, 71240, 81563, NA, 36723, NA, 6756...
## $ dataQualityGrade <chr> "A", "A", "A+", "D", "A+", "B", "A", "B...
## $ lastUpdateEt <chr> "10/9/2020 03:59", "10/9/2020 11:00", "...
## $ dateModified <dttm> 2020-10-09 03:59:00, 2020-10-09 11:00:...
## $ checkTimeEt <chr> "10/08 23:59", "10/09 07:00", "10/07 20...
## $ death <dbl> 60, 2653, 1503, 0, 5746, 16428, 1997, 4...
## $ hospitalized <dbl> NA, 17989, 5805, NA, 20199, NA, 7834, 1...
## $ dateChecked <dttm> 2020-10-09 03:59:00, 2020-10-09 11:00:...
## $ totalTestsViral <dbl> 493070, 1196452, 1111526, 1616, NA, 157...
## $ positiveTestsViral <dbl> 9256, NA, NA, NA, NA, NA, NA, NA, NA, N...
## $ negativeTestsViral <dbl> 483521, NA, 1025546, NA, NA, NA, NA, NA...
## $ positiveCasesViral <dbl> 10176, 143900, 85980, 0, 219247, 838606...
## $ deathConfirmed <dbl> 60, 2496, 1359, NA, 5460, NA, NA, 3631,...
## $ deathProbable <dbl> NA, 157, 144, NA, 286, NA, NA, 899, NA,...
## $ totalTestEncountersViral <dbl> NA, NA, NA, NA, NA, NA, 1496198, NA, 42...
## $ totalTestsPeopleViral <dbl> NA, NA, NA, NA, 1543445, NA, 984996, NA...
## $ totalTestsAntibody <dbl> NA, NA, NA, NA, 300333, NA, 172162, NA,...
## $ positiveTestsAntibody <dbl> NA, NA, NA, NA, NA, NA, 12116, NA, NA, ...
## $ negativeTestsAntibody <dbl> NA, NA, NA, NA, NA, NA, 160046, NA, NA,...
## $ totalTestsPeopleAntibody <dbl> NA, 59957, NA, NA, NA, NA, NA, NA, NA, ...
## $ positiveTestsPeopleAntibody <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,...
## $ negativeTestsPeopleAntibody <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,...
## $ totalTestsPeopleAntigen <dbl> NA, NA, 25538, NA, NA, NA, NA, NA, NA, ...
## $ positiveTestsPeopleAntigen <dbl> NA, NA, 4591, NA, NA, NA, NA, NA, NA, N...
## $ totalTestsAntigen <dbl> NA, NA, 21856, NA, NA, NA, NA, NA, NA, ...
## $ positiveTestsAntigen <dbl> NA, NA, 3300, NA, NA, NA, NA, NA, NA, N...
## $ fips <chr> "02", "01", "05", "60", "04", "06", "08...
## $ positiveIncrease <dbl> 180, 1490, 0, 0, 683, 3806, 863, 290, 7...
## $ negativeIncrease <dbl> 1719, 10506, 0, 0, 11699, 109068, 9322,...
## $ total <dbl> 493070, 1216017, 1115691, 1616, 1548282...
## $ totalTestResultsSource <chr> "posNeg", "posNeg", "posNeg", "posNeg",...
## $ totalTestResultsIncrease <dbl> 1899, 11742, 0, 0, 12350, 112874, 18897...
## $ posNeg <dbl> 493070, 1216017, 1115691, 1616, 1548282...
## $ deathIncrease <dbl> 0, 16, 0, 0, 3, 67, 7, 3, 0, 0, 118, 54...
## $ hospitalizedIncrease <dbl> 0, 0, 0, 0, 28, 0, 63, 0, 0, 0, 196, 12...
## $ hash <chr> "3c160d82e90df263b41bbb98bcc4d2f6d17ff9...
## $ commercialScore <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...
## $ negativeRegularScore <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...
## $ negativeScore <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...
## $ positiveScore <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...
## $ score <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...
## $ grade <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,...
##
##
## Control totals - note that validState other than TRUE will be discarded
##
## # A tibble: 2 x 6
## validState cases deaths hosp tests n
## <lgl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 FALSE 57271 800 NA 451888 1040
## 2 TRUE 7566265 204670 NA 112888209 11269
## Observations: 11,269
## Variables: 6
## $ date <date> 2020-10-09, 2020-10-09, 2020-10-09, 2020-10-09, 2020-10-09,...
## $ state <chr> "AK", "AL", "AR", "AZ", "CA", "CO", "CT", "DC", "DE", "FL", ...
## $ cases <dbl> 180, 1490, 0, 683, 3806, 863, 290, 78, 145, 2908, 1625, 101,...
## $ deaths <dbl> 0, 16, 0, 3, 67, 7, 3, 0, 0, 118, 54, 1, 16, 3, 32, 19, 40, ...
## $ hosp <dbl> 51, 816, 546, 706, 3186, 356, 134, 99, 104, 2143, 1717, 111,...
## $ tests <dbl> 1899, 11742, 0, 12350, 112874, 18897, 17281, 5409, 2865, 284...
## Observations: 11,269
## Variables: 14
## $ date <date> 2020-01-22, 2020-01-22, 2020-01-23, 2020-01-23, 2020-01-24,...
## $ state <chr> "MA", "WA", "MA", "WA", "MA", "WA", "MA", "WA", "MA", "WA", ...
## $ cases <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...
## $ deaths <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...
## $ hosp <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, ...
## $ tests <dbl> 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, ...
## $ cpm <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...
## $ dpm <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...
## $ hpm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, ...
## $ tpm <dbl> 0.0000000, 0.0000000, 0.1471796, 0.0000000, 0.0000000, 0.000...
## $ cpm7 <dbl> NA, NA, NA, NA, NA, NA, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...
## $ dpm7 <dbl> NA, NA, NA, NA, NA, NA, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...
## $ hpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, ...
## $ tpm7 <dbl> NA, NA, NA, NA, NA, NA, 0.04205130, 0.00000000, 0.06307695, ...
##
## Recency is defined as 2020-09-10 through current
##
## Recency is defined as 2020-09-10 through current
The segments appear to follow reasonable patterns:
- High impact, skewed early
- Medium impact, skewed early
- Medium impact, skewed late
- Small impact, skewed early (two segments are broadly in this category)
- Small impact, sustained
Comparisons of segment membership can also be made:
# Confirm that the names
if (!all.equal(names(test_hier5$useClusters), names(test_hier6_201010$useClusters))) {
stop("\nIssue with cluster names not matching\n")
}
# Create a data frame of segment changes
segChanges <- tibble::tibble(state=names(test_hier5$useClusters),
oldCluster=test_hier5$useClusters,
newCluster=test_hier6_201010$useClusters
)
# Counts of states by oldCluster and newCluster
segChanges %>%
count(oldCluster, newCluster)## # A tibble: 7 x 3
## oldCluster newCluster n
## <int> <int> <int>
## 1 1 1 13
## 2 2 2 8
## 3 3 1 5
## 4 3 3 4
## 5 3 6 8
## 6 4 4 4
## 7 5 5 9# Print states that changed clusters
segChanges %>%
filter(oldCluster != newCluster)## # A tibble: 13 x 3
## state oldCluster newCluster
## <chr> <int> <int>
## 1 IA 3 6
## 2 KS 3 1
## 3 KY 3 1
## 4 MN 3 6
## 5 MO 3 1
## 6 ND 3 1
## 7 NE 3 6
## 8 NH 3 6
## 9 NM 3 6
## 10 OH 3 6
## 11 SD 3 1
## 12 VA 3 6
## 13 WI 3 6What was previously one of the two low-impact segments has been split in to:
- CO, ME, VT, WA (new segment from split); and
- IA, MN, NE, NH, NM, OH, VA, WI (new segment from split); and
- KS, KY, MO, ND, SD (consolidates to other low-impact segment that has rising cases with stable deaths)
At a glance it seems reasonable, though the “lower impact” segments could theoretically all be consolidated without losing too much differentiation. That said, the dendrogram built with reasonable input parameters considers the split of the lower-impact segments to be more meaningful than the split of the moderate-early and moderate-late segments. And, the moderate-early and moderate-late split is valuable for analysis, so the decision is made to remain with the 6 hierarchical clusters as of the October 9, 2020 data.
Next, the USA Facts process is updated to be run using a single function, including the capability to download new data (function moved to Coronavirus_Statistics_Functions_v002.R):
# Run file for existing data to confirm functionality
tmpCty <- readRunUSAFacts(maxDate="2020-09-30",
popLoc="./RInputFiles/Coronavirus/covid_county_population_usafacts.csv",
caseLoc="./RInputFiles/Coronavirus/covid_confirmed_usafacts_downloaded_20201003.csv",
deathLoc="./RInputFiles/Coronavirus/covid_deaths_usafacts_downloaded_20201003.csv",
oldFile=burden_20200903_new,
existingCountyClusters=clustVec_county_20200903_new
)## Parsed with column specification:
## cols(
## countyFIPS = col_double(),
## `County Name` = col_character(),
## State = col_character(),
## population = col_double()
## )## Parsed with column specification:
## cols(
## .default = col_double(),
## `County Name` = col_character(),
## State = col_character()
## )## See spec(...) for full column specifications.## Observations: 811,530
## Variables: 6
## $ countyFIPS <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ countyName <chr> "Statewide Unallocated", "Statewide Unallocated", "State...
## $ state <chr> "AL", "AL", "AL", "AL", "AL", "AL", "AL", "AL", "AL", "A...
## $ stateFIPS <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,...
## $ date <date> 2020-01-22, 2020-01-23, 2020-01-24, 2020-01-25, 2020-01...
## $ cumCases <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...## Parsed with column specification:
## cols(
## .default = col_double(),
## `County Name` = col_character(),
## State = col_character()
## )
## See spec(...) for full column specifications.## Observations: 811,530
## Variables: 6
## $ countyFIPS <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ countyName <chr> "Statewide Unallocated", "Statewide Unallocated", "State...
## $ state <chr> "AL", "AL", "AL", "AL", "AL", "AL", "AL", "AL", "AL", "A...
## $ stateFIPS <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,...
## $ date <date> 2020-01-22, 2020-01-23, 2020-01-24, 2020-01-25, 2020-01...
## $ cumDeaths <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## # A tibble: 1 x 6
## cpm_mean_is0 dpm_mean_is0 cpm_mean_lt100 dpm_mean_lt100 cpm_mean_lt5000
## <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 0 0.0214 0 0.179 0.0761
## # ... with 1 more variable: dpm_mean_lt5000 <dbl>
##
## Recency is defined as 2020-09-01 through current
##
## Recency is defined as 2020-09-01 through current## Warning: Removed 1 rows containing missing values (geom_point).
## Joining, by = "fipsCounty"## Joining, by = "fipsCounty"
# Run subsets of the data
runUSAFactsSubsets(tmpCty, keyStates=c("CA", "TX", "FL", "NY", "IL", "PA"))
runUSAFactsSubsets(tmpCty,
lagMetrics=tibble::tibble(metric=c("pop", "dpm"),
n=c(3, 5),
minDate="2020-03-01",
maxDate="2020-09-30"
)
)##
## *** KEY COUNTIES INCLUDE: ***
## # A tibble: 15 x 5
## state cluster countyName dpm pop
## <chr> <fct> <chr> <dbl> <dbl>
## 1 06037 5 5 - Los Angeles (CA) 655. 10039107
## 2 17031 4 4 - Cook (IL) 1015. 5150233
## 3 48201 1 1 - Harris (TX) 551. 4713325
## 4 04013 4 4 - Maricopa (AZ) 756. 4485414
## 5 06073 2 2 - San Diego (CA) 235. 3338330
## 6 06059 1 1 - Orange (CA) 399. 3175692
## 7 12086 4 4 - Miami-Dade (FL) 1207. 2716940
## 8 48113 1 1 - Dallas (TX) 430. 2635516
## 9 36047 3 3 - Kings (NY) 2863. 2559903
## 10 36081 3 3 - Queens (NY) 3218. 2253858
## 11 53033 5 5 - King (WA) 337. 2252782
## 12 06085 2 2 - Santa Clara (CA) 167. 1927852
## 13 26163 3 3 - Wayne (MI) 1706. 1749343
## 14 06001 2 2 - Alameda (CA) 247. 1671329
## 15 39049 5 5 - Franklin (OH) 467. 1316756
##
## The best lags are:
## # A tibble: 15 x 5
## countyName state cluster corr lag
## <chr> <chr> <fct> <dbl> <int>
## 1 5 - Franklin (OH) 39049 5 0.218 0
## 2 5 - Los Angeles (CA) 06037 5 0.616 0
## 3 2 - San Diego (CA) 06073 2 0.646 2
## 4 5 - King (WA) 53033 5 0.198 2
## 5 4 - Cook (IL) 17031 4 0.836 5
## 6 3 - Kings (NY) 36047 3 0.965 8
## 7 3 - Queens (NY) 36081 3 0.941 8
## 8 1 - Dallas (TX) 48113 1 0.782 9
## 9 3 - Wayne (MI) 26163 3 0.844 10
## 10 1 - Harris (TX) 48201 1 0.740 13
## 11 4 - Maricopa (AZ) 04013 4 0.965 18
## 12 1 - Orange (CA) 06059 1 0.757 24
## 13 2 - Alameda (CA) 06001 2 0.727 24
## 14 2 - Santa Clara (CA) 06085 2 0.487 30
## 15 4 - Miami-Dade (FL) 12086 4 0.785 30## Warning: Removed 26 rows containing missing values (geom_path).## Warning: Removed 26 rows containing missing values (geom_path).
##
## *** KEY COUNTIES INCLUDE: ***
## # A tibble: 25 x 5
## state cluster countyName dpm pop
## <chr> <fct> <chr> <dbl> <dbl>
## 1 36005 3 3 - Bronx (NY) 3492. 1418207
## 2 36081 3 3 - Queens (NY) 3218. 2253858
## 3 36047 3 3 - Kings (NY) 2863. 2559903
## 4 34013 3 3 - Essex (NJ) 2663. 798975
## 5 34031 3 3 - Passaic (NJ) 2499. 501826
## 6 48215 4 4 - Hidalgo (TX) 1755. 868707
## 7 04027 4 4 - Yuma (AZ) 1614. 213787
## 8 22017 4 4 - Caddo (LA) 1503. 240204
## 9 44007 4 4 - Providence (RI) 1398. 638931
## 10 45041 4 4 - Florence (SC) 1381. 138293
## # ... with 15 more rows
##
## The best lags are:
## # A tibble: 25 x 5
## countyName state cluster corr lag
## <chr> <chr> <fct> <dbl> <int>
## 1 2 - Pulaski (AR) 05119 2 0.558 0
## 2 5 - Columbiana (OH) 39029 5 -0.0149 0
## 3 5 - Litchfield (CT) 09005 5 0.922 4
## 4 4 - Caddo (LA) 22017 4 0.210 5
## 5 3 - Bronx (NY) 36005 3 0.975 6
## 6 3 - Essex (NJ) 34013 3 0.876 7
## 7 3 - Kings (NY) 36047 3 0.965 8
## 8 3 - Queens (NY) 36081 3 0.941 8
## 9 4 - Florence (SC) 45041 4 0.856 8
## 10 3 - Passaic (NJ) 34031 3 0.810 9
## # ... with 15 more rows## Warning: Removed 158 rows containing missing values (geom_path).## Warning: Removed 158 rows containing missing values (geom_path).
# Confirm that outputs are identical
identical(tmpCty$pop, pop_usafacts)## [1] TRUEidentical(tmpCty$burdenData, burden_20201003)## [1] TRUEidentical(tmpCty$clusterData, clust_20201003)## [1] TRUEidentical(tmpCty$clustVec, clustVec_county_20200903_new)## [1] TRUEidentical(tmpCty$helperACC_county, helperACC_county_20201003)## [1] TRUEidentical(tmpCty$clusterStateData, clusterStateData_20201003)## [1] TRUEThe function as written can re-create previous results using existing data. Next steps are to enable data downloads and to create new segments using more recent data.
Data as of October 12, 2020 can be downloaded and processed against the existing segments:
caseLoc_20201012 <- "./RInputFiles/Coronavirus/covid_confirmed_usafacts_downloaded_20201012.csv"
deathLoc_20201012 <- "./RInputFiles/Coronavirus/covid_deaths_usafacts_downloaded_20201012.csv"
# Run file for existing data to confirm functionality
cty_20201012 <- readRunUSAFacts(maxDate="2020-10-10",
popLoc="./RInputFiles/Coronavirus/covid_county_population_usafacts.csv",
caseLoc=caseLoc_20201012,
deathLoc=deathLoc_20201012,
dlCaseDeath=TRUE,
oldFile=burden_20200903_new,
existingCountyClusters=clustVec_county_20200903_new
)## Parsed with column specification:
## cols(
## countyFIPS = col_double(),
## `County Name` = col_character(),
## State = col_character(),
## population = col_double()
## )## Parsed with column specification:
## cols(
## .default = col_double(),
## `County Name` = col_character(),
## State = col_character()
## )## See spec(...) for full column specifications.## Observations: 840,285
## Variables: 6
## $ countyFIPS <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ countyName <chr> "Statewide Unallocated", "Statewide Unallocated", "State...
## $ state <chr> "AL", "AL", "AL", "AL", "AL", "AL", "AL", "AL", "AL", "A...
## $ stateFIPS <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,...
## $ date <date> 2020-01-22, 2020-01-23, 2020-01-24, 2020-01-25, 2020-01...
## $ cumCases <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...## Parsed with column specification:
## cols(
## .default = col_double(),
## `County Name` = col_character(),
## State = col_character()
## )
## See spec(...) for full column specifications.## Observations: 840,285
## Variables: 6
## $ countyFIPS <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ countyName <chr> "Statewide Unallocated", "Statewide Unallocated", "State...
## $ state <chr> "AL", "AL", "AL", "AL", "AL", "AL", "AL", "AL", "AL", "A...
## $ stateFIPS <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,...
## $ date <date> 2020-01-22, 2020-01-23, 2020-01-24, 2020-01-25, 2020-01...
## $ cumDeaths <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## # A tibble: 1 x 6
## cpm_mean_is0 dpm_mean_is0 cpm_mean_lt100 dpm_mean_lt100 cpm_mean_lt5000
## <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 0 0.0189 0 0.154 0.0616
## # ... with 1 more variable: dpm_mean_lt5000 <dbl>
##
## Recency is defined as 2020-09-11 through current
##
## Recency is defined as 2020-09-11 through current
## Joining, by = "fipsCounty"## Joining, by = "fipsCounty"
Finally, the CDC data can be run using a single function:
# Create a mapping of epiweek to month (use 2020 for this)
epiMonth <- tibble::tibble(dt=as.Date("2020-01-01")+0:365,
month=lubridate::month(dt),
quarter=lubridate::quarter(dt),
ew=lubridate::epiweek(dt)
) %>%
count(ew, month, quarter) %>%
arrange(ew, -n) %>%
group_by(ew) %>%
summarize(month=factor(month.abb[first(month)], levels=month.abb), quarter=first(quarter))## `summarise()` ungrouping output (override with `.groups` argument)epiMonth## # A tibble: 53 x 3
## ew month quarter
## <dbl> <fct> <int>
## 1 1 Jan 1
## 2 2 Jan 1
## 3 3 Jan 1
## 4 4 Jan 1
## 5 5 Jan 1
## 6 6 Feb 1
## 7 7 Feb 1
## 8 8 Feb 1
## 9 9 Feb 1
## 10 10 Mar 1
## # ... with 43 more rows# Population by age bucket (estimated) for 2020
usPopBucket2020 <- survival::uspop2[, , "2020"] %>%
apply(1, FUN=sum) %>%
tibble::tibble(ageActual=as.integer(names(.)), pop_2020=.) %>%
mutate(age=factor(case_when(ageActual <= 24 ~ "Under 25 years",
ageActual <= 44 ~ "25-44 years",
ageActual <= 64 ~ "45-64 years",
ageActual <= 74 ~ "65-74 years",
ageActual <= 84 ~ "75-84 years",
TRUE ~ "85 years and older"
), levels=levels(cdc20200923$age)
)
) %>%
group_by(age) %>%
summarize(pop=sum(pop_2020))## `summarise()` ungrouping output (override with `.groups` argument)usPopBucket2020## # A tibble: 6 x 2
## age pop
## <fct> <int>
## 1 Under 25 years 112501697
## 2 25-44 years 89724301
## 3 45-64 years 84356197
## 4 65-74 years 32312186
## 5 75-84 years 15895265
## 6 85 years and older 6597019# Load and process the CDC data
cdcLoc <- "Weekly_counts_of_deaths_by_jurisdiction_and_age_group_downloaded_20200923.csv"
cdcList_20200923 <- readRunCDCAllCause(loc=cdcLoc,
dir="./RInputFiles/Coronavirus/",
startYear=2015,
curYear=2020,
weekThru=30,
startWeek=9,
lst=test_hier5,
epiMap=epiMonth,
cvDeathThru="2020-07-31",
cdcPlotStartWeek=10,
agePopData=usPopBucket2020
)## Rows: 174,311
## Columns: 11
## $ Jurisdiction <chr> "Alabama", "Alabama", "Alabama", "Alabama", "A...
## $ `Week Ending Date` <chr> "1/10/2015", "1/17/2015", "1/24/2015", "1/31/2...
## $ `State Abbreviation` <chr> "AL", "AL", "AL", "AL", "AL", "AL", "AL", "AL"...
## $ Year <int> 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015...
## $ Week <int> 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14,...
## $ `Age Group` <chr> "25-44 years", "25-44 years", "25-44 years", "...
## $ `Number of Deaths` <dbl> 67, 49, 55, 59, 47, 59, 41, 47, 59, 57, 54, 50...
## $ `Time Period` <chr> "2015-2019", "2015-2019", "2015-2019", "2015-2...
## $ Type <chr> "Predicted (weighted)", "Predicted (weighted)"...
## $ Suppress <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA...
## $ Note <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA...
## Rows: 174,311
## Columns: 11
## $ Jurisdiction <chr> "Alabama", "Alabama", "Alabama", "Alabama", "Alabama",...
## $ weekEnding <date> 2015-01-10, 2015-01-17, 2015-01-24, 2015-01-31, 2015-...
## $ state <chr> "AL", "AL", "AL", "AL", "AL", "AL", "AL", "AL", "AL", ...
## $ year <int> 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015, ...
## $ week <int> 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16,...
## $ age <chr> "25-44 years", "25-44 years", "25-44 years", "25-44 ye...
## $ deaths <dbl> 67, 49, 55, 59, 47, 59, 41, 47, 59, 57, 54, 50, 58, 42...
## $ period <chr> "2015-2019", "2015-2019", "2015-2019", "2015-2019", "2...
## $ type <chr> "Predicted (weighted)", "Predicted (weighted)", "Predi...
## $ Suppress <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA...
## $ Note <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA...
##
## Check Control Levels and Record Counts for Renamed Data:## `summarise()` ungrouping output (override with `.groups` argument)## # A tibble: 6 x 4
## age n n_deaths_na deaths
## <chr> <int> <int> <dbl>
## 1 25-44 years 26066 3 3170502
## 2 45-64 years 31884 7 12464677
## 3 65-74 years 31880 10 12347549
## 4 75-84 years 31902 11 15363924
## 5 85 years and older 31888 9 20067928
## 6 Under 25 years 20691 0 1372225## `summarise()` regrouping output by 'period', 'year' (override with `.groups` argument)## # A tibble: 12 x 6
## # Groups: period, year [6]
## period year type n n_deaths_na deaths
## <chr> <int> <chr> <int> <int> <dbl>
## 1 2015-2019 2015 Predicted (weighted) 15285 0 5416393
## 2 2015-2019 2015 Unweighted 15285 0 5416393
## 3 2015-2019 2016 Predicted (weighted) 15365 0 5483764
## 4 2015-2019 2016 Unweighted 15365 0 5483764
## 5 2015-2019 2017 Predicted (weighted) 15318 0 5643350
## 6 2015-2019 2017 Unweighted 15318 0 5643350
## 7 2015-2019 2018 Predicted (weighted) 15305 0 5698002
## 8 2015-2019 2018 Unweighted 15305 0 5698002
## 9 2015-2019 2019 Predicted (weighted) 15319 0 5725516
## 10 2015-2019 2019 Unweighted 15319 0 5725516
## 11 2020 2020 Predicted (weighted) 10586 24 4476340
## 12 2020 2020 Unweighted 10541 16 4376415## `summarise()` regrouping output by 'period' (override with `.groups` argument)## # A tibble: 3 x 5
## # Groups: period [2]
## period Suppress n n_deaths_na deaths
## <chr> <chr> <int> <int> <dbl>
## 1 2015-2019 <NA> 153184 0 5.59e7
## 2 2020 Suppressed (counts highly incomplete, <5~ 40 40 0.
## 3 2020 <NA> 21087 0 8.85e6## `summarise()` regrouping output by 'period' (override with `.groups` argument)## # A tibble: 9 x 5
## # Groups: period [2]
## period Note n n_deaths_na deaths
## <chr> <chr> <int> <int> <dbl>
## 1 2015-20~ <NA> 153184 0 5.59e7
## 2 2020 Data in recent weeks are incomplete. Only~ 16591 0 7.27e6
## 3 2020 Data in recent weeks are incomplete. Only~ 324 0 1.52e5
## 4 2020 Data in recent weeks are incomplete. Only~ 288 30 3.05e4
## 5 2020 Data in recent weeks are incomplete. Only~ 1502 10 4.17e5
## 6 2020 Data in recent weeks are incomplete. Only~ 60 0 2.71e4
## 7 2020 Estimates for Pennsylvania are too low fo~ 48 0 2.23e4
## 8 2020 Weights may be too low to account for und~ 436 0 1.40e5
## 9 2020 <NA> 1878 0 7.97e5## `summarise()` regrouping output by 'state' (override with `.groups` argument)## state Jurisdiction n n_deaths_na deaths
## 1 US United States 3552 0 32276762
## 2 CA California 3552 0 3072016
## 3 FL Florida 3552 0 2357528
## 4 TX Texas 3552 0 2314502
## 5 PA Pennsylvania 3552 0 1548716
## 6 OH Ohio 3552 0 1395014
## 7 IL Illinois 3552 0 1219119
## 8 NY New York 3552 0 1155790
## 9 MI Michigan 3552 0 1111203
## 10 NC North Carolina 3521 17 1051632
## 11 GA Georgia 3551 0 965504
## 12 NJ New Jersey 3546 0 867210
## 13 TN Tennessee 3552 0 840787
## 14 VA Virginia 3552 0 774383
## 15 IN Indiana 3550 0 749760
## 16 MO Missouri 3548 0 728220
## 17 MA Massachusetts 3516 0 685409
## 18 AZ Arizona 3552 0 684537
## 19 YC New York City 3548 0 671106
## 20 WA Washington 3551 0 645406
## 21 AL Alabama 3550 0 598526
## 22 WI Wisconsin 3533 0 592047
## 23 MD Maryland 3546 0 570238
## 24 SC South Carolina 3549 0 560415
## 25 KY Kentucky 3519 0 545032
## 26 LA Louisiana 3545 0 525668
## 27 MN Minnesota 3509 0 503567
## 28 CO Colorado 3550 0 446708
## 29 OK Oklahoma 3541 0 445362
## 30 OR Oregon 3382 0 413553
## 31 MS Mississippi 3488 0 363792
## 32 AR Arkansas 3444 0 361612
## 33 CT Connecticut 3106 13 356416
## 34 IA Iowa 3190 0 339791
## 35 PR Puerto Rico 3272 0 331654
## 36 KS Kansas 3246 0 296520
## 37 NV Nevada 3291 0 289275
## 38 WV West Virginia 3011 10 251046
## 39 UT Utah 3438 0 213931
## 40 NM New Mexico 3140 0 205026
## 41 NE Nebraska 2846 0 188492
## 42 ME Maine 2646 0 160341
## 43 ID Idaho 2766 0 152936
## 44 NH New Hampshire 2672 0 135110
## 45 HI Hawaii 2556 0 124379
## 46 RI Rhode Island 2474 0 114274
## 47 MT Montana 2556 0 109719
## 48 DE Delaware 2558 0 99625
## 49 SD South Dakota 2448 0 86461
## 50 ND North Dakota 2433 0 75079
## 51 DC District of Columbia 2545 0 64202
## 52 VT Vermont 2336 0 61925
## 53 WY Wyoming 2318 0 47230
## 54 AK Alaska 2352 0 42249## `summarise()` regrouping output by 'year' (override with `.groups` argument)## Rows: 174,311
## Columns: 11
## $ Jurisdiction <chr> "Alabama", "Alabama", "Alabama", "Alabama", "Alabama",...
## $ weekEnding <date> 2015-01-10, 2015-01-17, 2015-01-24, 2015-01-31, 2015-...
## $ state <chr> "AL", "AL", "AL", "AL", "AL", "AL", "AL", "AL", "AL", ...
## $ year <fct> 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015, ...
## $ week <int> 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16,...
## $ age <fct> 25-44 years, 25-44 years, 25-44 years, 25-44 years, 25...
## $ deaths <dbl> 67, 49, 55, 59, 47, 59, 41, 47, 59, 57, 54, 50, 58, 42...
## $ period <fct> 2015-2019, 2015-2019, 2015-2019, 2015-2019, 2015-2019,...
## $ type <chr> "Predicted (weighted)", "Predicted (weighted)", "Predi...
## $ Suppress <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA...
## $ Note <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA...
## Rows: 85,466
## Columns: 11
## $ Jurisdiction <chr> "Alabama", "Alabama", "Alabama", "Alabama", "Alabama",...
## $ weekEnding <date> 2015-01-10, 2015-01-17, 2015-01-24, 2015-01-31, 2015-...
## $ state <chr> "AL", "AL", "AL", "AL", "AL", "AL", "AL", "AL", "AL", ...
## $ year <fct> 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015, ...
## $ week <int> 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16,...
## $ age <fct> 25-44 years, 25-44 years, 25-44 years, 25-44 years, 25...
## $ deaths <dbl> 67, 49, 55, 59, 47, 59, 41, 47, 59, 57, 54, 50, 58, 42...
## $ period <fct> 2015-2019, 2015-2019, 2015-2019, 2015-2019, 2015-2019,...
## $ type <chr> "Predicted (weighted)", "Predicted (weighted)", "Predi...
## $ Suppress <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA...
## $ Note <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA...
##
##
## *** Data suppression checks ***
## # A tibble: 0 x 11
## # ... with 11 variables: Jurisdiction <chr>, weekEnding <date>, state <chr>,
## # year <fct>, week <int>, age <fct>, deaths <dbl>, period <fct>, type <chr>,
## # Suppress <chr>, Note <chr>
##
## Data suppression checks OK - 0 records in current week/year suppressed## `summarise()` regrouping output by 'Jurisdiction', 'weekEnding', 'state', 'year', 'week', 'age', 'period', 'type' (override with `.groups` argument)## Rows: 80,379
## Columns: 12
## $ Jurisdiction <chr> "Alabama", "Alabama", "Alabama", "Alabama", "Alabama",...
## $ weekEnding <date> 2015-01-10, 2015-01-10, 2015-01-10, 2015-01-10, 2015-...
## $ state <chr> "AL", "AL", "AL", "AL", "AL", "AL", "AL", "AL", "AL", ...
## $ year <fct> 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015, ...
## $ week <int> 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, ...
## $ age <fct> Under 25 years, 25-44 years, 45-64 years, 65-74 years,...
## $ period <fct> 2015-2019, 2015-2019, 2015-2019, 2015-2019, 2015-2019,...
## $ type <chr> "Predicted (weighted)", "Predicted (weighted)", "Predi...
## $ Suppress <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA...
## $ n <int> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...
## $ deaths <dbl> 25, 67, 253, 202, 272, 320, 28, 49, 256, 222, 253, 332...
## $ Note <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA...
##
## First duplicate is in row number (0 means no duplicates): 0## `summarise()` ungrouping output (override with `.groups` argument)## `summarise()` regrouping output by 'cluster' (override with `.groups` argument)## `summarise()` ungrouping output (override with `.groups` argument)
## `summarise()` regrouping output by 'year' (override with `.groups` argument)
## `summarise()` regrouping output by 'year', 'week' (override with `.groups` argument)
## `summarise()` regrouping output by 'year', 'week' (override with `.groups` argument)
## `summarise()` regrouping output by 'year', 'age', 'week' (override with `.groups` argument)
## `summarise()` regrouping output by 'year' (override with `.groups` argument)
## `summarise()` ungrouping output (override with `.groups` argument)
## `summarise()` regrouping output by 'year' (override with `.groups` argument)
## `summarise()` regrouping output by 'year' (override with `.groups` argument)
## `summarise()` regrouping output by 'year' (override with `.groups` argument)
## `summarise()` regrouping output by 'year' (override with `.groups` argument)
## `summarise()` regrouping output by 'year' (override with `.groups` argument)
## `summarise()` regrouping output by 'year' (override with `.groups` argument)## `summarise()` regrouping output by 'year' (override with `.groups` argument)
## `summarise()` regrouping output by 'year' (override with `.groups` argument)
## `summarise()` regrouping output by 'year' (override with `.groups` argument)
## `summarise()` regrouping output by 'year' (override with `.groups` argument)
## `summarise()` regrouping output by 'year' (override with `.groups` argument)
## `summarise()` regrouping output by 'year' (override with `.groups` argument)
## `summarise()` regrouping output by 'year' (override with `.groups` argument)
## `summarise()` regrouping output by 'year' (override with `.groups` argument)
## `summarise()` regrouping output by 'year' (override with `.groups` argument)
## `summarise()` regrouping output by 'year' (override with `.groups` argument)
## `summarise()` regrouping output by 'year' (override with `.groups` argument)
## `summarise()` regrouping output by 'year' (override with `.groups` argument)
## `summarise()` regrouping output by 'year' (override with `.groups` argument)
## `summarise()` regrouping output by 'year' (override with `.groups` argument)
## `summarise()` regrouping output by 'year' (override with `.groups` argument)
## `summarise()` regrouping output by 'year' (override with `.groups` argument)
## `summarise()` regrouping output by 'year' (override with `.groups` argument)
## `summarise()` regrouping output by 'year' (override with `.groups` argument)
## `summarise()` regrouping output by 'year' (override with `.groups` argument)
## `summarise()` regrouping output by 'year' (override with `.groups` argument)
## `summarise()` regrouping output by 'year' (override with `.groups` argument)
## `summarise()` regrouping output by 'year' (override with `.groups` argument)
## `summarise()` regrouping output by 'year' (override with `.groups` argument)
## `summarise()` regrouping output by 'year' (override with `.groups` argument)
## `summarise()` regrouping output by 'year' (override with `.groups` argument)
## `summarise()` regrouping output by 'year' (override with `.groups` argument)
## `summarise()` regrouping output by 'year' (override with `.groups` argument)
## `summarise()` regrouping output by 'year' (override with `.groups` argument)
## `summarise()` regrouping output by 'year' (override with `.groups` argument)
## `summarise()` regrouping output by 'year' (override with `.groups` argument)
## `summarise()` regrouping output by 'year' (override with `.groups` argument)
## `summarise()` regrouping output by 'year' (override with `.groups` argument)
## `summarise()` regrouping output by 'year' (override with `.groups` argument)
## `summarise()` regrouping output by 'year' (override with `.groups` argument)
## `summarise()` regrouping output by 'year' (override with `.groups` argument)
## `summarise()` regrouping output by 'year' (override with `.groups` argument)
## `summarise()` regrouping output by 'year' (override with `.groups` argument)
## `summarise()` regrouping output by 'year' (override with `.groups` argument)
## `summarise()` regrouping output by 'year' (override with `.groups` argument)
## `summarise()` regrouping output by 'year' (override with `.groups` argument)
## `summarise()` regrouping output by 'year' (override with `.groups` argument)
## `summarise()` regrouping output by 'year' (override with `.groups` argument)
## `summarise()` regrouping output by 'year' (override with `.groups` argument)
## `summarise()` regrouping output by 'year' (override with `.groups` argument)
## `summarise()` regrouping output by 'year' (override with `.groups` argument)
## `summarise()` regrouping output by 'year' (override with `.groups` argument)
## `summarise()` regrouping output by 'year' (override with `.groups` argument)
## `summarise()` regrouping output by 'year' (override with `.groups` argument)
## `summarise()` regrouping output by 'year' (override with `.groups` argument)
## `summarise()` regrouping output by 'year' (override with `.groups` argument)## `summarise()` regrouping output by 'state', 'quarter', 'month' (override with `.groups` argument)## `summarise()` regrouping output by 'state' (override with `.groups` argument)
## `summarise()` regrouping output by 'state' (override with `.groups` argument)
## `summarise()` ungrouping output (override with `.groups` argument)## Joining, by = "state"
## Warning: Removed 1 rows containing missing values (position_stack).## Warning: Removed 1 rows containing missing values (geom_text).## `summarise()` regrouping output by 'year' (override with `.groups` argument)
## `summarise()` regrouping output by 'year' (override with `.groups` argument)
## `summarise()` regrouping output by 'year' (override with `.groups` argument)
## `summarise()` regrouping output by 'year' (override with `.groups` argument)
## `summarise()` regrouping output by 'year' (override with `.groups` argument)
## `summarise()` regrouping output by 'year' (override with `.groups` argument)
## `summarise()` regrouping output by 'age', 'quarter', 'month' (override with `.groups` argument)## `summarise()` regrouping output by 'age' (override with `.groups` argument)
## `summarise()` ungrouping output (override with `.groups` argument)
# Download latest CDC data and process
cdcLoc <- "Weekly_counts_of_deaths_by_jurisdiction_and_age_group_downloaded_20201014.csv"
cdcList_20201014 <- readRunCDCAllCause(loc=cdcLoc,
dir="./RInputFiles/Coronavirus/",
startYear=2015,
curYear=2020,
weekThru=34,
startWeek=9,
lst=test_hier5_201001,
epiMap=epiMonth,
cvDeathThru="2020-08-22",
cdcPlotStartWeek=10,
agePopData=usPopBucket2020,
dlData=TRUE,
ovrWriteError=FALSE
)##
## File: ./RInputFiles/Coronavirus/Weekly_counts_of_deaths_by_jurisdiction_and_age_group_downloaded_20201014.csv already exists
##
## File is NOT downloaded again
## Rows: 176,119
## Columns: 11
## $ Jurisdiction <chr> "Alabama", "Alabama", "Alabama", "Alabama", "A...
## $ `Week Ending Date` <chr> "01/10/2015", "01/17/2015", "01/24/2015", "01/...
## $ `State Abbreviation` <chr> "AL", "AL", "AL", "AL", "AL", "AL", "AL", "AL"...
## $ Year <int> 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015...
## $ Week <int> 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14,...
## $ `Age Group` <chr> "25-44 years", "25-44 years", "25-44 years", "...
## $ `Number of Deaths` <dbl> 67, 49, 55, 59, 47, 59, 41, 47, 59, 57, 54, 50...
## $ `Time Period` <chr> "2015-2019", "2015-2019", "2015-2019", "2015-2...
## $ Type <chr> "Predicted (weighted)", "Predicted (weighted)"...
## $ Suppress <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA...
## $ Note <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA...
## Rows: 176,119
## Columns: 11
## $ Jurisdiction <chr> "Alabama", "Alabama", "Alabama", "Alabama", "Alabama",...
## $ weekEnding <date> 2015-01-10, 2015-01-17, 2015-01-24, 2015-01-31, 2015-...
## $ state <chr> "AL", "AL", "AL", "AL", "AL", "AL", "AL", "AL", "AL", ...
## $ year <int> 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015, ...
## $ week <int> 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16,...
## $ age <chr> "25-44 years", "25-44 years", "25-44 years", "25-44 ye...
## $ deaths <dbl> 67, 49, 55, 59, 47, 59, 41, 47, 59, 57, 54, 50, 58, 42...
## $ period <chr> "2015-2019", "2015-2019", "2015-2019", "2015-2019", "2...
## $ type <chr> "Predicted (weighted)", "Predicted (weighted)", "Predi...
## $ Suppress <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA...
## $ Note <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA...
##
## Check Control Levels and Record Counts for Renamed Data:## `summarise()` ungrouping output (override with `.groups` argument)## # A tibble: 6 x 4
## age n n_deaths_na deaths
## <chr> <int> <int> <dbl>
## 1 25-44 years 26364 0 3214460
## 2 45-64 years 32211 4 12612924
## 3 65-74 years 32200 5 12500875
## 4 75-84 years 32221 6 15546198
## 5 85 years and older 32210 7 20287067
## 6 Under 25 years 20913 0 1388384## `summarise()` regrouping output by 'period', 'year' (override with `.groups` argument)## # A tibble: 12 x 6
## # Groups: period, year [6]
## period year type n n_deaths_na deaths
## <chr> <int> <chr> <int> <int> <dbl>
## 1 2015-2019 2015 Predicted (weighted) 15285 0 5416393
## 2 2015-2019 2015 Unweighted 15285 0 5416393
## 3 2015-2019 2016 Predicted (weighted) 15365 0 5483764
## 4 2015-2019 2016 Unweighted 15365 0 5483764
## 5 2015-2019 2017 Predicted (weighted) 15319 0 5643363
## 6 2015-2019 2017 Unweighted 15319 0 5643363
## 7 2015-2019 2018 Predicted (weighted) 15305 0 5698004
## 8 2015-2019 2018 Unweighted 15305 0 5698004
## 9 2015-2019 2019 Predicted (weighted) 15317 0 5725524
## 10 2015-2019 2019 Unweighted 15317 0 5725524
## 11 2020 2020 Predicted (weighted) 11490 13 4858272
## 12 2020 2020 Unweighted 11447 9 4757540## `summarise()` regrouping output by 'period' (override with `.groups` argument)## # A tibble: 3 x 5
## # Groups: period [2]
## period Suppress n n_deaths_na deaths
## <chr> <chr> <int> <int> <dbl>
## 1 2015-2019 <NA> 153182 0 5.59e7
## 2 2020 Suppressed (counts highly incomplete, <5~ 22 22 0.
## 3 2020 <NA> 22915 0 9.62e6## `summarise()` regrouping output by 'period' (override with `.groups` argument)## # A tibble: 10 x 5
## # Groups: period [2]
## period Note n n_deaths_na deaths
## <chr> <chr> <int> <int> <dbl>
## 1 2015-20~ <NA> 153182 0 5.59e7
## 2 2020 Data in recent weeks are incomplete. Onl~ 18444 0 8.13e6
## 3 2020 Data in recent weeks are incomplete. Onl~ 360 0 1.68e5
## 4 2020 Data in recent weeks are incomplete. Onl~ 382 19 4.45e4
## 5 2020 Data in recent weeks are incomplete. Onl~ 1329 3 2.88e5
## 6 2020 Data in recent weeks are incomplete. Onl~ 60 0 2.79e4
## 7 2020 Estimates for Pennsylvania are too low f~ 36 0 1.68e4
## 8 2020 Weights may be too low to account for un~ 284 0 7.19e4
## 9 2020 Weights may be too low to account for un~ 12 0 5.72e3
## 10 2020 <NA> 2030 0 8.65e5## `summarise()` regrouping output by 'state' (override with `.groups` argument)## state Jurisdiction n n_deaths_na deaths
## 1 US United States 3588 0 32655734
## 2 CA California 3588 0 3108728
## 3 FL Florida 3588 0 2387134
## 4 TX Texas 3588 0 2348924
## 5 PA Pennsylvania 3588 0 1566142
## 6 OH Ohio 3588 0 1411315
## 7 IL Illinois 3588 0 1232847
## 8 NY New York 3588 0 1167718
## 9 MI Michigan 3588 0 1123550
## 10 NC North Carolina 3536 8 1059363
## 11 GA Georgia 3588 0 978193
## 12 NJ New Jersey 3580 0 875776
## 13 TN Tennessee 3588 0 851531
## 14 VA Virginia 3588 0 782913
## 15 IN Indiana 3587 0 758488
## 16 MO Missouri 3586 0 737546
## 17 AZ Arizona 3588 0 692460
## 18 MA Massachusetts 3554 0 692109
## 19 YC New York City 3584 0 676973
## 20 WA Washington 3588 0 652928
## 21 AL Alabama 3585 0 606105
## 22 WI Wisconsin 3570 0 598878
## 23 MD Maryland 3582 0 576822
## 24 SC South Carolina 3586 0 568254
## 25 KY Kentucky 3553 0 552088
## 26 LA Louisiana 3577 0 532758
## 27 MN Minnesota 3547 0 509530
## 28 CO Colorado 3586 0 451948
## 29 OK Oklahoma 3576 0 450316
## 30 OR Oregon 3418 0 418422
## 31 MS Mississippi 3522 0 368372
## 32 AR Arkansas 3484 0 366066
## 33 CT Connecticut 3146 9 360662
## 34 IA Iowa 3224 0 343678
## 35 PR Puerto Rico 3305 0 336026
## 36 KS Kansas 3282 0 300158
## 37 NV Nevada 3327 0 292844
## 38 WV West Virginia 3033 3 253132
## 39 UT Utah 3474 0 216546
## 40 NM New Mexico 3170 0 207657
## 41 NE Nebraska 2878 0 190649
## 42 ME Maine 2676 0 162212
## 43 ID Idaho 2796 0 154882
## 44 NH New Hampshire 2700 0 136571
## 45 HI Hawaii 2588 0 125932
## 46 RI Rhode Island 2504 0 115701
## 47 MT Montana 2584 0 111052
## 48 DE Delaware 2590 0 100672
## 49 SD South Dakota 2478 0 87630
## 50 ND North Dakota 2459 0 75823
## 51 DC District of Columbia 2571 0 64792
## 52 VT Vermont 2362 0 62663
## 53 WY Wyoming 2348 0 47960
## 54 AK Alaska 2379 2 42735## `summarise()` regrouping output by 'year' (override with `.groups` argument)## Rows: 176,119
## Columns: 11
## $ Jurisdiction <chr> "Alabama", "Alabama", "Alabama", "Alabama", "Alabama",...
## $ weekEnding <date> 2015-01-10, 2015-01-17, 2015-01-24, 2015-01-31, 2015-...
## $ state <chr> "AL", "AL", "AL", "AL", "AL", "AL", "AL", "AL", "AL", ...
## $ year <fct> 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015, ...
## $ week <int> 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16,...
## $ age <fct> 25-44 years, 25-44 years, 25-44 years, 25-44 years, 25...
## $ deaths <dbl> 67, 49, 55, 59, 47, 59, 41, 47, 59, 57, 54, 50, 58, 42...
## $ period <fct> 2015-2019, 2015-2019, 2015-2019, 2015-2019, 2015-2019,...
## $ type <chr> "Predicted (weighted)", "Predicted (weighted)", "Predi...
## $ Suppress <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA...
## $ Note <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA...
## Rows: 86,663
## Columns: 11
## $ Jurisdiction <chr> "Alabama", "Alabama", "Alabama", "Alabama", "Alabama",...
## $ weekEnding <date> 2015-01-10, 2015-01-17, 2015-01-24, 2015-01-31, 2015-...
## $ state <chr> "AL", "AL", "AL", "AL", "AL", "AL", "AL", "AL", "AL", ...
## $ year <fct> 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015, ...
## $ week <int> 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16,...
## $ age <fct> 25-44 years, 25-44 years, 25-44 years, 25-44 years, 25...
## $ deaths <dbl> 67, 49, 55, 59, 47, 59, 41, 47, 59, 57, 54, 50, 58, 42...
## $ period <fct> 2015-2019, 2015-2019, 2015-2019, 2015-2019, 2015-2019,...
## $ type <chr> "Predicted (weighted)", "Predicted (weighted)", "Predi...
## $ Suppress <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA...
## $ Note <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA...
##
##
## *** Data suppression checks ***
## # A tibble: 0 x 11
## # ... with 11 variables: Jurisdiction <chr>, weekEnding <date>, state <chr>,
## # year <fct>, week <int>, age <fct>, deaths <dbl>, period <fct>, type <chr>,
## # Suppress <chr>, Note <chr>
##
## Data suppression checks OK - 0 records in current week/year suppressed## `summarise()` regrouping output by 'Jurisdiction', 'weekEnding', 'state', 'year', 'week', 'age', 'period', 'type' (override with `.groups` argument)## Rows: 81,507
## Columns: 12
## $ Jurisdiction <chr> "Alabama", "Alabama", "Alabama", "Alabama", "Alabama",...
## $ weekEnding <date> 2015-01-10, 2015-01-10, 2015-01-10, 2015-01-10, 2015-...
## $ state <chr> "AL", "AL", "AL", "AL", "AL", "AL", "AL", "AL", "AL", ...
## $ year <fct> 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015, ...
## $ week <int> 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, ...
## $ age <fct> Under 25 years, 25-44 years, 45-64 years, 65-74 years,...
## $ period <fct> 2015-2019, 2015-2019, 2015-2019, 2015-2019, 2015-2019,...
## $ type <chr> "Predicted (weighted)", "Predicted (weighted)", "Predi...
## $ Suppress <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA...
## $ n <int> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...
## $ deaths <dbl> 25, 67, 253, 202, 272, 320, 28, 49, 256, 222, 253, 332...
## $ Note <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA...
##
## First duplicate is in row number (0 means no duplicates): 0## `summarise()` ungrouping output (override with `.groups` argument)## `summarise()` regrouping output by 'cluster' (override with `.groups` argument)## `summarise()` ungrouping output (override with `.groups` argument)
## `summarise()` regrouping output by 'year' (override with `.groups` argument)
## `summarise()` regrouping output by 'year', 'week' (override with `.groups` argument)
## `summarise()` regrouping output by 'year', 'week' (override with `.groups` argument)
## `summarise()` regrouping output by 'year', 'age', 'week' (override with `.groups` argument)
## `summarise()` regrouping output by 'year' (override with `.groups` argument)
## `summarise()` ungrouping output (override with `.groups` argument)
## `summarise()` regrouping output by 'year' (override with `.groups` argument)
## `summarise()` regrouping output by 'year' (override with `.groups` argument)
## `summarise()` regrouping output by 'year' (override with `.groups` argument)
## `summarise()` regrouping output by 'year' (override with `.groups` argument)
## `summarise()` regrouping output by 'year' (override with `.groups` argument)
## `summarise()` regrouping output by 'year' (override with `.groups` argument)## `summarise()` regrouping output by 'year' (override with `.groups` argument)
## `summarise()` regrouping output by 'year' (override with `.groups` argument)
## `summarise()` regrouping output by 'year' (override with `.groups` argument)
## `summarise()` regrouping output by 'year' (override with `.groups` argument)
## `summarise()` regrouping output by 'year' (override with `.groups` argument)
## `summarise()` regrouping output by 'year' (override with `.groups` argument)
## `summarise()` regrouping output by 'year' (override with `.groups` argument)
## `summarise()` regrouping output by 'year' (override with `.groups` argument)
## `summarise()` regrouping output by 'year' (override with `.groups` argument)
## `summarise()` regrouping output by 'year' (override with `.groups` argument)
## `summarise()` regrouping output by 'year' (override with `.groups` argument)
## `summarise()` regrouping output by 'year' (override with `.groups` argument)
## `summarise()` regrouping output by 'year' (override with `.groups` argument)
## `summarise()` regrouping output by 'year' (override with `.groups` argument)
## `summarise()` regrouping output by 'year' (override with `.groups` argument)
## `summarise()` regrouping output by 'year' (override with `.groups` argument)
## `summarise()` regrouping output by 'year' (override with `.groups` argument)
## `summarise()` regrouping output by 'year' (override with `.groups` argument)
## `summarise()` regrouping output by 'year' (override with `.groups` argument)
## `summarise()` regrouping output by 'year' (override with `.groups` argument)
## `summarise()` regrouping output by 'year' (override with `.groups` argument)
## `summarise()` regrouping output by 'year' (override with `.groups` argument)
## `summarise()` regrouping output by 'year' (override with `.groups` argument)
## `summarise()` regrouping output by 'year' (override with `.groups` argument)
## `summarise()` regrouping output by 'year' (override with `.groups` argument)
## `summarise()` regrouping output by 'year' (override with `.groups` argument)
## `summarise()` regrouping output by 'year' (override with `.groups` argument)
## `summarise()` regrouping output by 'year' (override with `.groups` argument)
## `summarise()` regrouping output by 'year' (override with `.groups` argument)
## `summarise()` regrouping output by 'year' (override with `.groups` argument)
## `summarise()` regrouping output by 'year' (override with `.groups` argument)
## `summarise()` regrouping output by 'year' (override with `.groups` argument)
## `summarise()` regrouping output by 'year' (override with `.groups` argument)
## `summarise()` regrouping output by 'year' (override with `.groups` argument)
## `summarise()` regrouping output by 'year' (override with `.groups` argument)
## `summarise()` regrouping output by 'year' (override with `.groups` argument)
## `summarise()` regrouping output by 'year' (override with `.groups` argument)
## `summarise()` regrouping output by 'year' (override with `.groups` argument)
## `summarise()` regrouping output by 'year' (override with `.groups` argument)
## `summarise()` regrouping output by 'year' (override with `.groups` argument)
## `summarise()` regrouping output by 'year' (override with `.groups` argument)
## `summarise()` regrouping output by 'year' (override with `.groups` argument)
## `summarise()` regrouping output by 'year' (override with `.groups` argument)
## `summarise()` regrouping output by 'year' (override with `.groups` argument)
## `summarise()` regrouping output by 'year' (override with `.groups` argument)
## `summarise()` regrouping output by 'year' (override with `.groups` argument)
## `summarise()` regrouping output by 'year' (override with `.groups` argument)
## `summarise()` regrouping output by 'year' (override with `.groups` argument)
## `summarise()` regrouping output by 'year' (override with `.groups` argument)
## `summarise()` regrouping output by 'year' (override with `.groups` argument)## `summarise()` regrouping output by 'state', 'quarter', 'month' (override with `.groups` argument)## `summarise()` regrouping output by 'state' (override with `.groups` argument)
## `summarise()` regrouping output by 'state' (override with `.groups` argument)
## `summarise()` ungrouping output (override with `.groups` argument)## Joining, by = "state"
## Warning: Removed 1 rows containing missing values (position_stack).
## Warning: Removed 1 rows containing missing values (geom_text).## `summarise()` regrouping output by 'year' (override with `.groups` argument)
## `summarise()` regrouping output by 'year' (override with `.groups` argument)
## `summarise()` regrouping output by 'year' (override with `.groups` argument)
## `summarise()` regrouping output by 'year' (override with `.groups` argument)
## `summarise()` regrouping output by 'year' (override with `.groups` argument)
## `summarise()` regrouping output by 'year' (override with `.groups` argument)
## `summarise()` regrouping output by 'age', 'quarter', 'month' (override with `.groups` argument)## `summarise()` regrouping output by 'age' (override with `.groups` argument)
## `summarise()` ungrouping output (override with `.groups` argument)
Next, a capability for saving and reading .RDS files is included, and key files from previous analyses are saved:
# File used for comparison in readRunCOVIDTrackingProject
saveToRDS(test_hier5_201001, ovrWriteError=FALSE)
# Files used for comparison in readRunUSAFacts
saveToRDS(burden_20200903_new, ovrWriteError=FALSE)
saveToRDS(clustVec_county_20200903_new, ovrWriteError=FALSE)
# Files used for CDC all-cause deaths
saveToRDS(epiMonth, ovrWriteError=FALSE)
saveToRDS(usPopBucket2020, ovrWriteError=FALSE)The full process of downloading new data and creating new segments is then:
- Download new data from COVID Tracking Project and create 6 clusters (can update parameters if the dendrogram and plots do not look acceptable)
- Download new data from USA Facts and create 5 clusters (can updated parameters if the charts do not look acceptable)
- Download new data from CDC and run analyses against it
Example code includes:
# Create segments and download data from COVID Tracking Project
locDownload <- "./RInputFiles/Coronavirus/CV_downloaded_201015.csv"
test_hier6_201014 <- readRunCOVIDTrackingProject(thruLabel="Oct 14, 2020",
downloadTo=if(file.exists(locDownload)) NULL else locDownload,
readFrom=locDownload,
compareFile=readFromRDS("test_hier5_201001")$dfRaw,
hierarchical=TRUE,
kCut=6,
minShape=3,
ratioDeathvsCase = 5,
ratioTotalvsShape = 0.5,
minDeath=100,
minCase=10000
)## Parsed with column specification:
## cols(
## .default = col_double(),
## state = col_character(),
## dataQualityGrade = col_character(),
## lastUpdateEt = col_character(),
## dateModified = col_datetime(format = ""),
## checkTimeEt = col_character(),
## dateChecked = col_datetime(format = ""),
## fips = col_character(),
## totalTestResultsSource = col_character(),
## hash = col_character(),
## grade = col_logical()
## )## See spec(...) for full column specifications.##
## File is unique by state and date
##
##
## Overall control totals in file:
## # A tibble: 1 x 3
## positiveIncrease deathIncrease hospitalizedCurrently
## <dbl> <dbl> <dbl>
## 1 7876282 208389 8292248
##
## *** COMPARISONS TO REFERENCE FILE: compareFile
##
## Checkin for similarity of: column names
## In reference but not in current:
## In current but not in reference: probableCases
##
## Checkin for similarity of: states
## In reference but not in current:
## In current but not in reference:
##
## Checkin for similarity of: dates
## In reference but not in current:
## In current but not in reference: 2020-10-14 2020-10-13 2020-10-12 2020-10-11 2020-10-10 2020-10-09 2020-10-08 2020-10-07 2020-10-06 2020-10-05 2020-10-04 2020-10-03 2020-10-02 2020-10-01
##
## *** Difference of at least 5 and difference is at least 1%:## Joining, by = c("date", "name")## [1] date name newValue oldValue
## <0 rows> (or 0-length row.names)## Joining, by = c("date", "name")## Warning: Removed 14 rows containing missing values (geom_path).##
##
## *** Difference of at least 5 and difference is at least 1%:## Joining, by = c("state", "name")
## state name newValue oldValue
## 1 HI positiveIncrease 12469 12289
## Observations: 12,589
## Variables: 55
## $ date <date> 2020-10-14, 2020-10-14, 2020-10-14, 20...
## $ state <chr> "AK", "AL", "AR", "AS", "AZ", "CA", "CO...
## $ positive <dbl> 11183, 167977, 95246, 0, 227635, 855072...
## $ probableCases <dbl> NA, 20232, 5011, NA, 5043, NA, 5430, 24...
## $ negative <dbl> 505994, 1078476, 1083107, 1616, 1366610...
## $ pending <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,...
## $ totalTestResults <dbl> 517177, 1226221, 1173342, 1616, 1589202...
## $ hospitalizedCurrently <dbl> 56, 834, 582, NA, 711, 3126, 385, 188, ...
## $ hospitalizedCumulative <dbl> NA, 18635, 6148, NA, 20366, NA, 8003, 1...
## $ inIcuCurrently <dbl> NA, NA, 241, NA, 163, 753, NA, NA, 24, ...
## $ inIcuCumulative <dbl> NA, 1907, NA, NA, NA, NA, NA, NA, NA, N...
## $ onVentilatorCurrently <dbl> 7, NA, 112, NA, 85, NA, NA, NA, 7, NA, ...
## $ onVentilatorCumulative <dbl> NA, 1085, 755, NA, NA, NA, NA, NA, NA, ...
## $ recovered <dbl> 5950, 74238, 85597, NA, 37586, NA, 6922...
## $ dataQualityGrade <chr> "A", "A", "A+", "D", "A+", "B", "A", "B...
## $ lastUpdateEt <chr> "10/14/2020 03:59", "10/14/2020 11:00",...
## $ dateModified <dttm> 2020-10-14 03:59:00, 2020-10-14 11:00:...
## $ checkTimeEt <chr> "10/13 23:59", "10/14 07:00", "10/13 20...
## $ death <dbl> 64, 2706, 1634, 0, 5772, 16639, 2009, 4...
## $ hospitalized <dbl> NA, 18635, 6148, NA, 20366, NA, 8003, 1...
## $ dateChecked <dttm> 2020-10-14 03:59:00, 2020-10-14 11:00:...
## $ totalTestsViral <dbl> 517177, 1226221, 1173342, 1616, NA, 164...
## $ positiveTestsViral <dbl> 10109, NA, NA, NA, NA, NA, NA, NA, NA, ...
## $ negativeTestsViral <dbl> 506755, NA, 1083107, NA, NA, NA, NA, NA...
## $ positiveCasesViral <dbl> 11183, 147745, 90235, 0, 222592, 855072...
## $ deathConfirmed <dbl> 64, 2549, 1484, NA, 5485, NA, NA, 3637,...
## $ deathProbable <dbl> NA, 157, 150, NA, 287, NA, NA, 900, NA,...
## $ totalTestEncountersViral <dbl> NA, NA, NA, NA, NA, NA, 1588632, NA, 44...
## $ totalTestsPeopleViral <dbl> NA, NA, NA, NA, 1589202, NA, 1032545, N...
## $ totalTestsAntibody <dbl> NA, NA, NA, NA, 303875, NA, 174566, NA,...
## $ positiveTestsAntibody <dbl> NA, NA, NA, NA, NA, NA, 12313, NA, NA, ...
## $ negativeTestsAntibody <dbl> NA, NA, NA, NA, NA, NA, 162253, NA, NA,...
## $ totalTestsPeopleAntibody <dbl> NA, 60687, NA, NA, NA, NA, NA, NA, NA, ...
## $ positiveTestsPeopleAntibody <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,...
## $ negativeTestsPeopleAntibody <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,...
## $ totalTestsPeopleAntigen <dbl> NA, NA, 31294, NA, NA, NA, NA, NA, NA, ...
## $ positiveTestsPeopleAntigen <dbl> NA, NA, 5537, NA, NA, NA, NA, NA, NA, N...
## $ totalTestsAntigen <dbl> NA, NA, 21856, NA, NA, NA, NA, NA, NA, ...
## $ positiveTestsAntigen <dbl> NA, NA, 3300, NA, NA, NA, NA, NA, NA, N...
## $ fips <chr> "02", "01", "05", "60", "04", "06", "08...
## $ positiveIncrease <dbl> 144, 784, 1079, 0, 901, 2666, 1048, 164...
## $ negativeIncrease <dbl> 2244, 4352, 9793, 0, 7263, 89104, 8218,...
## $ total <dbl> 517177, 1246453, 1178353, 1616, 1594245...
## $ totalTestResultsSource <chr> "posNeg", "posNeg", "posNeg", "posNeg",...
## $ totalTestResultsIncrease <dbl> 2388, 5014, 10677, 0, 8051, 91770, 1730...
## $ posNeg <dbl> 517177, 1246453, 1178353, 1616, 1594245...
## $ deathIncrease <dbl> 4, 41, 23, 0, 5, 58, 11, 4, 1, 1, 66, 1...
## $ hospitalizedIncrease <dbl> 0, 195, 79, 0, 54, 0, 88, 0, 0, 0, 258,...
## $ hash <chr> "3e715172385e995a3ce6d9b2672d1b675141de...
## $ commercialScore <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...
## $ negativeRegularScore <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...
## $ negativeScore <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...
## $ positiveScore <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...
## $ score <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...
## $ grade <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,...
##
##
## Control totals - note that validState other than TRUE will be discarded
##
## # A tibble: 2 x 6
## validState cases deaths hosp tests n
## <lgl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 FALSE 60253 825 NA 457677 1065
## 2 TRUE 7816029 207564 NA 117909065 11524
## Observations: 11,524
## Variables: 6
## $ date <date> 2020-10-14, 2020-10-14, 2020-10-14, 2020-10-14, 2020-10-14,...
## $ state <chr> "AK", "AL", "AR", "AZ", "CA", "CO", "CT", "DC", "DE", "FL", ...
## $ cases <dbl> 144, 784, 1079, 901, 2666, 1048, 164, 64, 71, 2883, 1297, 61...
## $ deaths <dbl> 4, 41, 23, 5, 58, 11, 4, 1, 1, 66, 16, 4, 12, 2, 48, 14, 67,...
## $ hosp <dbl> 56, 834, 582, 711, 3126, 385, 188, 86, 116, 2155, 1705, 109,...
## $ tests <dbl> 2388, 5014, 10677, 8051, 91770, 17303, 8376, 5181, 1071, 218...
## Observations: 11,524
## Variables: 14
## $ date <date> 2020-01-22, 2020-01-22, 2020-01-23, 2020-01-23, 2020-01-24,...
## $ state <chr> "MA", "WA", "MA", "WA", "MA", "WA", "MA", "WA", "MA", "WA", ...
## $ cases <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...
## $ deaths <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...
## $ hosp <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, ...
## $ tests <dbl> 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, ...
## $ cpm <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...
## $ dpm <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...
## $ hpm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, ...
## $ tpm <dbl> 0.0000000, 0.0000000, 0.1471796, 0.0000000, 0.0000000, 0.000...
## $ cpm7 <dbl> NA, NA, NA, NA, NA, NA, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...
## $ dpm7 <dbl> NA, NA, NA, NA, NA, NA, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...
## $ hpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, ...
## $ tpm7 <dbl> NA, NA, NA, NA, NA, NA, 0.04205130, 0.00000000, 0.06307695, ...
##
## Recency is defined as 2020-09-15 through current
##
## Recency is defined as 2020-09-15 through current
# Create segments and download data from USA Facts
caseLoc <- "./RInputFiles/Coronavirus/covid_confirmed_usafacts_downloaded_20201015.csv"
deathLoc <- "./RInputFiles/Coronavirus/covid_deaths_usafacts_downloaded_20201015.csv"
# Run file for existing data to confirm functionality
cty_20201015 <- readRunUSAFacts(maxDate="2020-10-13",
popLoc="./RInputFiles/Coronavirus/covid_county_population_usafacts.csv",
caseLoc=caseLoc,
deathLoc=deathLoc,
dlCaseDeath=!(file.exists(caseLoc) & file.exists(deathLoc)),
oldFile=readFromRDS("burden_20200903_new"),
existingStateClusters=test_hier6_201014$useClusters,
createClusters=TRUE,
minShape=3,
ratioDeathvsCase = 5,
ratioTotalvsShape = 0.5,
minDeath=100,
minCase=5000,
nCenters=5,
testCenters=1:25,
iter.max=20,
nstart=10,
seed=2010151358
)## Parsed with column specification:
## cols(
## countyFIPS = col_double(),
## `County Name` = col_character(),
## State = col_character(),
## population = col_double()
## )## Parsed with column specification:
## cols(
## .default = col_double(),
## `County Name` = col_character(),
## State = col_character()
## )## See spec(...) for full column specifications.## Observations: 849,870
## Variables: 6
## $ countyFIPS <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ countyName <chr> "Statewide Unallocated", "Statewide Unallocated", "State...
## $ state <chr> "AL", "AL", "AL", "AL", "AL", "AL", "AL", "AL", "AL", "A...
## $ stateFIPS <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,...
## $ date <date> 2020-01-22, 2020-01-23, 2020-01-24, 2020-01-25, 2020-01...
## $ cumCases <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...## Parsed with column specification:
## cols(
## .default = col_double(),
## `County Name` = col_character(),
## State = col_character()
## )
## See spec(...) for full column specifications.## Observations: 849,870
## Variables: 6
## $ countyFIPS <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ countyName <chr> "Statewide Unallocated", "Statewide Unallocated", "State...
## $ state <chr> "AL", "AL", "AL", "AL", "AL", "AL", "AL", "AL", "AL", "A...
## $ stateFIPS <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,...
## $ date <date> 2020-01-22, 2020-01-23, 2020-01-24, 2020-01-25, 2020-01...
## $ cumDeaths <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## # A tibble: 1 x 6
## cpm_mean_is0 dpm_mean_is0 cpm_mean_lt100 dpm_mean_lt100 cpm_mean_lt5000
## <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 0 0.0157 0 0.147 0.0559
## # ... with 1 more variable: dpm_mean_lt5000 <dbl>
##
## Cluster means and counts
## 1 2 3 4 5
## . 277.00 73.00 240.00 529.00 472.00
## totalCases 0.68 1.47 1.22 0.90 0.50
## totalDeaths 2.32 10.02 5.13 1.91 0.59
## cases_3 0.02 0.05 0.01 0.01 0.01
## deaths_3 0.08 0.09 0.04 0.03 0.07
## cases_4 0.15 0.25 0.10 0.03 0.05
## deaths_4 1.34 1.48 0.67 0.23 0.56
## cases_5 0.14 0.13 0.10 0.04 0.06
## deaths_5 1.63 0.95 0.78 0.21 0.38
## cases_6 0.08 0.09 0.11 0.09 0.07
## deaths_6 0.65 0.51 0.53 0.29 0.38
## cases_7 0.15 0.20 0.26 0.25 0.17
## deaths_7 0.37 0.59 0.80 0.76 0.59
## cases_8 0.16 0.15 0.20 0.24 0.18
## deaths_8 0.37 0.79 1.07 1.41 0.55
## cases_9 0.17 0.09 0.15 0.23 0.23
## deaths_9 0.36 0.47 0.83 1.51 0.67
## cases_10 0.11 0.05 0.07 0.12 0.16
## deaths_10 0.20 0.14 0.29 0.54 0.60
##
## Recency is defined as 2020-09-14 through current
##
## Recency is defined as 2020-09-14 through current## Warning: Removed 1 rows containing missing values (geom_point).
## Joining, by = "fipsCounty"## Joining, by = "fipsCounty"
# Download latest CDC data and process
cdcLoc <- "Weekly_counts_of_deaths_by_jurisdiction_and_age_group_downloaded_20201015.csv"
cdcList_20201015 <- readRunCDCAllCause(loc=cdcLoc,
startYear=2015,
curYear=2020,
weekThru=34,
startWeek=9,
lst=test_hier6_201014,
epiMap=readFromRDS("epiMonth"),
cvDeathThru="2020-08-22",
cdcPlotStartWeek=10,
agePopData=readFromRDS("usPopBucket2020"),
dlData=TRUE,
ovrWriteError=FALSE
)##
## Downloading CDC data from https://data.cdc.gov/api/views/y5bj-9g5w/rows.csv?accessType=DOWNLOAD to ./RInputFiles/Coronavirus/Weekly_counts_of_deaths_by_jurisdiction_and_age_group_downloaded_20201015.csv
## Observations: 176,686
## Variables: 11
## $ Jurisdiction <chr> "Alabama", "Alabama", "Alabama", "Alabama", "A...
## $ `Week Ending Date` <chr> "01/10/2015", "01/17/2015", "01/24/2015", "01/...
## $ `State Abbreviation` <chr> "AL", "AL", "AL", "AL", "AL", "AL", "AL", "AL"...
## $ Year <int> 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015...
## $ Week <int> 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14,...
## $ `Age Group` <chr> "25-44 years", "25-44 years", "25-44 years", "...
## $ `Number of Deaths` <dbl> 67, 49, 55, 59, 47, 59, 41, 47, 59, 57, 54, 50...
## $ `Time Period` <chr> "2015-2019", "2015-2019", "2015-2019", "2015-2...
## $ Type <chr> "Predicted (weighted)", "Predicted (weighted)"...
## $ Suppress <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA...
## $ Note <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA...
## Observations: 176,686
## Variables: 11
## $ Jurisdiction <chr> "Alabama", "Alabama", "Alabama", "Alabama", "Alabama",...
## $ weekEnding <date> 2015-01-10, 2015-01-17, 2015-01-24, 2015-01-31, 2015-...
## $ state <chr> "AL", "AL", "AL", "AL", "AL", "AL", "AL", "AL", "AL", ...
## $ year <int> 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015, ...
## $ week <int> 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16,...
## $ age <chr> "25-44 years", "25-44 years", "25-44 years", "25-44 ye...
## $ deaths <dbl> 67, 49, 55, 59, 47, 59, 41, 47, 59, 57, 54, 50, 58, 42...
## $ period <chr> "2015-2019", "2015-2019", "2015-2019", "2015-2019", "2...
## $ type <chr> "Predicted (weighted)", "Predicted (weighted)", "Predi...
## $ Suppress <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA...
## $ Note <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA...
##
## Check Control Levels and Record Counts for Renamed Data:
## # A tibble: 6 x 4
## age n n_deaths_na deaths
## <chr> <int> <int> <dbl>
## 1 25-44 years 26446 1 3226901
## 2 45-64 years 32314 4 12655629
## 3 65-74 years 32308 4 12546354
## 4 75-84 years 32327 5 15599787
## 5 85 years and older 32314 5 20352070
## 6 Under 25 years 20977 0 1392852
## # A tibble: 12 x 6
## # Groups: period, year [6]
## period year type n n_deaths_na deaths
## <chr> <int> <chr> <int> <int> <dbl>
## 1 2015-2019 2015 Predicted (weighted) 15287 0 5416412
## 2 2015-2019 2015 Unweighted 15287 0 5416412
## 3 2015-2019 2016 Predicted (weighted) 15365 0 5483764
## 4 2015-2019 2016 Unweighted 15365 0 5483764
## 5 2015-2019 2017 Predicted (weighted) 15317 0 5643337
## 6 2015-2019 2017 Unweighted 15317 0 5643337
## 7 2015-2019 2018 Predicted (weighted) 15305 0 5698007
## 8 2015-2019 2018 Unweighted 15305 0 5698007
## 9 2015-2019 2019 Predicted (weighted) 15317 0 5725524
## 10 2015-2019 2019 Unweighted 15317 0 5725524
## 11 2020 2020 Predicted (weighted) 11773 13 4969185
## 12 2020 2020 Unweighted 11731 6 4870320
## # A tibble: 3 x 5
## # Groups: period [2]
## period Suppress n n_deaths_na deaths
## <chr> <chr> <int> <int> <dbl>
## 1 2015-2019 <NA> 153182 0 5.59e7
## 2 2020 Suppressed (counts highly incomplete, <5~ 19 19 0.
## 3 2020 <NA> 23485 0 9.84e6
## # A tibble: 10 x 5
## # Groups: period [2]
## period Note n n_deaths_na deaths
## <chr> <chr> <int> <int> <dbl>
## 1 2015-20~ <NA> 153182 0 5.59e7
## 2 2020 Data in recent weeks are incomplete. Onl~ 18954 0 8.34e6
## 3 2020 Data in recent weeks are incomplete. Onl~ 420 0 1.96e5
## 4 2020 Data in recent weeks are incomplete. Onl~ 394 16 4.47e4
## 5 2020 Data in recent weeks are incomplete. Onl~ 1362 3 2.99e5
## 6 2020 Data in recent weeks are incomplete. Onl~ 12 0 5.54e3
## 7 2020 Estimates for Pennsylvania are too low f~ 36 0 1.69e4
## 8 2020 Weights may be too low to account for un~ 248 0 6.06e4
## 9 2020 Weights may be too low to account for un~ 12 0 5.69e3
## 10 2020 <NA> 2066 0 8.77e5
## state Jurisdiction n n_deaths_na deaths
## 1 US United States 3600 0 32767366
## 2 CA California 3600 0 3119418
## 3 FL Florida 3600 0 2395987
## 4 TX Texas 3600 0 2358271
## 5 PA Pennsylvania 3600 0 1571585
## 6 OH Ohio 3600 0 1416195
## 7 IL Illinois 3600 0 1237033
## 8 NY New York 3600 0 1171620
## 9 MI Michigan 3599 0 1125676
## 10 NC North Carolina 3536 8 1060294
## 11 GA Georgia 3600 0 982315
## 12 NJ New Jersey 3594 0 878338
## 13 TN Tennessee 3600 0 855255
## 14 VA Virginia 3600 0 785789
## 15 IN Indiana 3598 0 760849
## 16 MO Missouri 3597 0 740140
## 17 AZ Arizona 3600 0 694914
## 18 MA Massachusetts 3568 0 694218
## 19 YC New York City 3596 0 678875
## 20 WA Washington 3600 0 655215
## 21 AL Alabama 3596 0 608067
## 22 WI Wisconsin 3581 0 601074
## 23 MD Maryland 3594 0 578852
## 24 SC South Carolina 3598 0 570629
## 25 KY Kentucky 3564 0 554152
## 26 LA Louisiana 3590 0 534961
## 27 MN Minnesota 3557 0 511091
## 28 CO Colorado 3598 0 453533
## 29 OK Oklahoma 3590 0 452162
## 30 OR Oregon 3429 0 419882
## 31 MS Mississippi 3533 0 369852
## 32 AR Arkansas 3497 0 367683
## 33 CT Connecticut 3155 8 361512
## 34 IA Iowa 3232 0 344962
## 35 PR Puerto Rico 3317 0 336978
## 36 KS Kansas 3292 0 301264
## 37 NV Nevada 3339 0 293938
## 38 WV West Virginia 3042 3 253845
## 39 UT Utah 3486 0 217243
## 40 NM New Mexico 3180 0 208428
## 41 NE Nebraska 2886 0 190958
## 42 ME Maine 2686 0 162773
## 43 ID Idaho 2804 0 155435
## 44 NH New Hampshire 2708 0 137002
## 45 HI Hawaii 2596 0 126342
## 46 RI Rhode Island 2513 0 116090
## 47 MT Montana 2592 0 111506
## 48 DE Delaware 2599 0 101066
## 49 SD South Dakota 2485 0 87752
## 50 ND North Dakota 2467 0 76289
## 51 DC District of Columbia 2581 0 65035
## 52 VT Vermont 2370 0 62844
## 53 WY Wyoming 2356 0 48148
## 54 AK Alaska 2385 0 42892
## Observations: 176,686
## Variables: 11
## $ Jurisdiction <chr> "Alabama", "Alabama", "Alabama", "Alabama", "Alabama",...
## $ weekEnding <date> 2015-01-10, 2015-01-17, 2015-01-24, 2015-01-31, 2015-...
## $ state <chr> "AL", "AL", "AL", "AL", "AL", "AL", "AL", "AL", "AL", ...
## $ year <fct> 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015, ...
## $ week <int> 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16,...
## $ age <fct> 25-44 years, 25-44 years, 25-44 years, 25-44 years, 25...
## $ deaths <dbl> 67, 49, 55, 59, 47, 59, 41, 47, 59, 57, 54, 50, 58, 42...
## $ period <fct> 2015-2019, 2015-2019, 2015-2019, 2015-2019, 2015-2019,...
## $ type <chr> "Predicted (weighted)", "Predicted (weighted)", "Predi...
## $ Suppress <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA...
## $ Note <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA...
## Observations: 86,667
## Variables: 11
## $ Jurisdiction <chr> "Alabama", "Alabama", "Alabama", "Alabama", "Alabama",...
## $ weekEnding <date> 2015-01-10, 2015-01-17, 2015-01-24, 2015-01-31, 2015-...
## $ state <chr> "AL", "AL", "AL", "AL", "AL", "AL", "AL", "AL", "AL", ...
## $ year <fct> 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015, ...
## $ week <int> 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16,...
## $ age <fct> 25-44 years, 25-44 years, 25-44 years, 25-44 years, 25...
## $ deaths <dbl> 67, 49, 55, 59, 47, 59, 41, 47, 59, 57, 54, 50, 58, 42...
## $ period <fct> 2015-2019, 2015-2019, 2015-2019, 2015-2019, 2015-2019,...
## $ type <chr> "Predicted (weighted)", "Predicted (weighted)", "Predi...
## $ Suppress <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA...
## $ Note <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA...
##
## Confirm that data suppressions and NA deaths have been aliminated:
## # A tibble: 0 x 11
## # ... with 11 variables: Jurisdiction <chr>, weekEnding <date>, state <chr>,
## # year <fct>, week <int>, age <fct>, deaths <dbl>, period <fct>, type <chr>,
## # Suppress <chr>, Note <chr>
## Observations: 81,511
## Variables: 12
## $ Jurisdiction <chr> "Alabama", "Alabama", "Alabama", "Alabama", "Alabama",...
## $ weekEnding <date> 2015-01-10, 2015-01-10, 2015-01-10, 2015-01-10, 2015-...
## $ state <chr> "AL", "AL", "AL", "AL", "AL", "AL", "AL", "AL", "AL", ...
## $ year <fct> 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015, ...
## $ week <int> 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, ...
## $ age <fct> Under 25 years, 25-44 years, 45-64 years, 65-74 years,...
## $ period <fct> 2015-2019, 2015-2019, 2015-2019, 2015-2019, 2015-2019,...
## $ type <chr> "Predicted (weighted)", "Predicted (weighted)", "Predi...
## $ Suppress <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA...
## $ n <int> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...
## $ deaths <dbl> 25, 67, 253, 202, 272, 320, 28, 49, 256, 222, 253, 332...
## $ Note <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA...
##
## First duplicate is in row number (0 means no duplicates): 0
## Warning: Column `state` joining factor and character vector, coercing into
## character vector
## Joining, by = "state"
## Warning: Removed 1 rows containing missing values (position_stack).## Warning: Removed 1 rows containing missing values (geom_text).
The process should be able to run in a stand-alone fashion, with any data needed available to be loaded from the saved RDS files.
The process can also be run using existing data and segments:
# Use existing segments with different data (already downloaded)
locDownload <- "./RInputFiles/Coronavirus/CV_downloaded_201015.csv"
test_old_201014 <- readRunCOVIDTrackingProject(thruLabel="Oct 14, 2020",
readFrom=locDownload,
compareFile=readFromRDS("test_hier5_201001")$dfRaw,
useClusters=readFromRDS("test_hier5_201001")$useClusters
)## Parsed with column specification:
## cols(
## .default = col_double(),
## state = col_character(),
## dataQualityGrade = col_character(),
## lastUpdateEt = col_character(),
## dateModified = col_datetime(format = ""),
## checkTimeEt = col_character(),
## dateChecked = col_datetime(format = ""),
## fips = col_character(),
## totalTestResultsSource = col_character(),
## hash = col_character(),
## grade = col_logical()
## )## See spec(...) for full column specifications.##
## File is unique by state and date
##
##
## Overall control totals in file:
## # A tibble: 1 x 3
## positiveIncrease deathIncrease hospitalizedCurrently
## <dbl> <dbl> <dbl>
## 1 7876282 208389 8292248
##
## *** COMPARISONS TO REFERENCE FILE: compareFile
##
## Checkin for similarity of: column names
## In reference but not in current:
## In current but not in reference: probableCases
##
## Checkin for similarity of: states
## In reference but not in current:
## In current but not in reference:
##
## Checkin for similarity of: dates
## In reference but not in current:
## In current but not in reference: 2020-10-14 2020-10-13 2020-10-12 2020-10-11 2020-10-10 2020-10-09 2020-10-08 2020-10-07 2020-10-06 2020-10-05 2020-10-04 2020-10-03 2020-10-02 2020-10-01
##
## *** Difference of at least 5 and difference is at least 1%:## Joining, by = c("date", "name")## [1] date name newValue oldValue
## <0 rows> (or 0-length row.names)## Joining, by = c("date", "name")## Warning: Removed 14 rows containing missing values (geom_path).##
##
## *** Difference of at least 5 and difference is at least 1%:## Joining, by = c("state", "name")
## state name newValue oldValue
## 1 HI positiveIncrease 12469 12289
## Observations: 12,589
## Variables: 55
## $ date <date> 2020-10-14, 2020-10-14, 2020-10-14, 20...
## $ state <chr> "AK", "AL", "AR", "AS", "AZ", "CA", "CO...
## $ positive <dbl> 11183, 167977, 95246, 0, 227635, 855072...
## $ probableCases <dbl> NA, 20232, 5011, NA, 5043, NA, 5430, 24...
## $ negative <dbl> 505994, 1078476, 1083107, 1616, 1366610...
## $ pending <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,...
## $ totalTestResults <dbl> 517177, 1226221, 1173342, 1616, 1589202...
## $ hospitalizedCurrently <dbl> 56, 834, 582, NA, 711, 3126, 385, 188, ...
## $ hospitalizedCumulative <dbl> NA, 18635, 6148, NA, 20366, NA, 8003, 1...
## $ inIcuCurrently <dbl> NA, NA, 241, NA, 163, 753, NA, NA, 24, ...
## $ inIcuCumulative <dbl> NA, 1907, NA, NA, NA, NA, NA, NA, NA, N...
## $ onVentilatorCurrently <dbl> 7, NA, 112, NA, 85, NA, NA, NA, 7, NA, ...
## $ onVentilatorCumulative <dbl> NA, 1085, 755, NA, NA, NA, NA, NA, NA, ...
## $ recovered <dbl> 5950, 74238, 85597, NA, 37586, NA, 6922...
## $ dataQualityGrade <chr> "A", "A", "A+", "D", "A+", "B", "A", "B...
## $ lastUpdateEt <chr> "10/14/2020 03:59", "10/14/2020 11:00",...
## $ dateModified <dttm> 2020-10-14 03:59:00, 2020-10-14 11:00:...
## $ checkTimeEt <chr> "10/13 23:59", "10/14 07:00", "10/13 20...
## $ death <dbl> 64, 2706, 1634, 0, 5772, 16639, 2009, 4...
## $ hospitalized <dbl> NA, 18635, 6148, NA, 20366, NA, 8003, 1...
## $ dateChecked <dttm> 2020-10-14 03:59:00, 2020-10-14 11:00:...
## $ totalTestsViral <dbl> 517177, 1226221, 1173342, 1616, NA, 164...
## $ positiveTestsViral <dbl> 10109, NA, NA, NA, NA, NA, NA, NA, NA, ...
## $ negativeTestsViral <dbl> 506755, NA, 1083107, NA, NA, NA, NA, NA...
## $ positiveCasesViral <dbl> 11183, 147745, 90235, 0, 222592, 855072...
## $ deathConfirmed <dbl> 64, 2549, 1484, NA, 5485, NA, NA, 3637,...
## $ deathProbable <dbl> NA, 157, 150, NA, 287, NA, NA, 900, NA,...
## $ totalTestEncountersViral <dbl> NA, NA, NA, NA, NA, NA, 1588632, NA, 44...
## $ totalTestsPeopleViral <dbl> NA, NA, NA, NA, 1589202, NA, 1032545, N...
## $ totalTestsAntibody <dbl> NA, NA, NA, NA, 303875, NA, 174566, NA,...
## $ positiveTestsAntibody <dbl> NA, NA, NA, NA, NA, NA, 12313, NA, NA, ...
## $ negativeTestsAntibody <dbl> NA, NA, NA, NA, NA, NA, 162253, NA, NA,...
## $ totalTestsPeopleAntibody <dbl> NA, 60687, NA, NA, NA, NA, NA, NA, NA, ...
## $ positiveTestsPeopleAntibody <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,...
## $ negativeTestsPeopleAntibody <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,...
## $ totalTestsPeopleAntigen <dbl> NA, NA, 31294, NA, NA, NA, NA, NA, NA, ...
## $ positiveTestsPeopleAntigen <dbl> NA, NA, 5537, NA, NA, NA, NA, NA, NA, N...
## $ totalTestsAntigen <dbl> NA, NA, 21856, NA, NA, NA, NA, NA, NA, ...
## $ positiveTestsAntigen <dbl> NA, NA, 3300, NA, NA, NA, NA, NA, NA, N...
## $ fips <chr> "02", "01", "05", "60", "04", "06", "08...
## $ positiveIncrease <dbl> 144, 784, 1079, 0, 901, 2666, 1048, 164...
## $ negativeIncrease <dbl> 2244, 4352, 9793, 0, 7263, 89104, 8218,...
## $ total <dbl> 517177, 1246453, 1178353, 1616, 1594245...
## $ totalTestResultsSource <chr> "posNeg", "posNeg", "posNeg", "posNeg",...
## $ totalTestResultsIncrease <dbl> 2388, 5014, 10677, 0, 8051, 91770, 1730...
## $ posNeg <dbl> 517177, 1246453, 1178353, 1616, 1594245...
## $ deathIncrease <dbl> 4, 41, 23, 0, 5, 58, 11, 4, 1, 1, 66, 1...
## $ hospitalizedIncrease <dbl> 0, 195, 79, 0, 54, 0, 88, 0, 0, 0, 258,...
## $ hash <chr> "3e715172385e995a3ce6d9b2672d1b675141de...
## $ commercialScore <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...
## $ negativeRegularScore <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...
## $ negativeScore <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...
## $ positiveScore <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...
## $ score <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...
## $ grade <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,...
##
##
## Control totals - note that validState other than TRUE will be discarded
##
## # A tibble: 2 x 6
## validState cases deaths hosp tests n
## <lgl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 FALSE 60253 825 NA 457677 1065
## 2 TRUE 7816029 207564 NA 117909065 11524
## Observations: 11,524
## Variables: 6
## $ date <date> 2020-10-14, 2020-10-14, 2020-10-14, 2020-10-14, 2020-10-14,...
## $ state <chr> "AK", "AL", "AR", "AZ", "CA", "CO", "CT", "DC", "DE", "FL", ...
## $ cases <dbl> 144, 784, 1079, 901, 2666, 1048, 164, 64, 71, 2883, 1297, 61...
## $ deaths <dbl> 4, 41, 23, 5, 58, 11, 4, 1, 1, 66, 16, 4, 12, 2, 48, 14, 67,...
## $ hosp <dbl> 56, 834, 582, 711, 3126, 385, 188, 86, 116, 2155, 1705, 109,...
## $ tests <dbl> 2388, 5014, 10677, 8051, 91770, 17303, 8376, 5181, 1071, 218...
## Observations: 11,524
## Variables: 14
## $ date <date> 2020-01-22, 2020-01-22, 2020-01-23, 2020-01-23, 2020-01-24,...
## $ state <chr> "MA", "WA", "MA", "WA", "MA", "WA", "MA", "WA", "MA", "WA", ...
## $ cases <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...
## $ deaths <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...
## $ hosp <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, ...
## $ tests <dbl> 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, ...
## $ cpm <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...
## $ dpm <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...
## $ hpm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, ...
## $ tpm <dbl> 0.0000000, 0.0000000, 0.1471796, 0.0000000, 0.0000000, 0.000...
## $ cpm7 <dbl> NA, NA, NA, NA, NA, NA, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...
## $ dpm7 <dbl> NA, NA, NA, NA, NA, NA, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...
## $ hpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, ...
## $ tpm7 <dbl> NA, NA, NA, NA, NA, NA, 0.04205130, 0.00000000, 0.06307695, ...
##
## Recency is defined as 2020-09-15 through current
##
## Recency is defined as 2020-09-15 through current
A similar approach can be taken with the county data:
# Previously downloaded data from USA Facts
caseLoc <- "./RInputFiles/Coronavirus/covid_confirmed_usafacts_downloaded_20201015.csv"
deathLoc <- "./RInputFiles/Coronavirus/covid_deaths_usafacts_downloaded_20201015.csv"
# Use existing segments with different data (already downloaded)
old_20201015 <- readRunUSAFacts(maxDate="2020-10-13",
popLoc="./RInputFiles/Coronavirus/covid_county_population_usafacts.csv",
caseLoc=caseLoc,
deathLoc=deathLoc,
oldFile=readFromRDS("burden_20200903_new"),
existingStateClusters=readFromRDS("test_hier5_201001")$useClusters,
existingCountyClusters=readFromRDS("clustVec_county_20200903_new"),
createClusters=FALSE
)## Parsed with column specification:
## cols(
## countyFIPS = col_double(),
## `County Name` = col_character(),
## State = col_character(),
## population = col_double()
## )## Parsed with column specification:
## cols(
## .default = col_double(),
## `County Name` = col_character(),
## State = col_character()
## )## See spec(...) for full column specifications.## Observations: 849,870
## Variables: 6
## $ countyFIPS <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ countyName <chr> "Statewide Unallocated", "Statewide Unallocated", "State...
## $ state <chr> "AL", "AL", "AL", "AL", "AL", "AL", "AL", "AL", "AL", "A...
## $ stateFIPS <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,...
## $ date <date> 2020-01-22, 2020-01-23, 2020-01-24, 2020-01-25, 2020-01...
## $ cumCases <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...## Parsed with column specification:
## cols(
## .default = col_double(),
## `County Name` = col_character(),
## State = col_character()
## )
## See spec(...) for full column specifications.## Observations: 849,870
## Variables: 6
## $ countyFIPS <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ countyName <chr> "Statewide Unallocated", "Statewide Unallocated", "State...
## $ state <chr> "AL", "AL", "AL", "AL", "AL", "AL", "AL", "AL", "AL", "A...
## $ stateFIPS <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,...
## $ date <date> 2020-01-22, 2020-01-23, 2020-01-24, 2020-01-25, 2020-01...
## $ cumDeaths <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## # A tibble: 1 x 6
## cpm_mean_is0 dpm_mean_is0 cpm_mean_lt100 dpm_mean_lt100 cpm_mean_lt5000
## <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 0 0.0157 0 0.147 0.0559
## # ... with 1 more variable: dpm_mean_lt5000 <dbl>
##
## Recency is defined as 2020-09-14 through current
##
## Recency is defined as 2020-09-14 through current## Warning: Removed 1 rows containing missing values (geom_point).
## Joining, by = "fipsCounty"## Joining, by = "fipsCounty"
A similar approach can be taken with the CDC all-cause deaths data:
# Use data that have previously been downloaded
cdcLoc <- "Weekly_counts_of_deaths_by_jurisdiction_and_age_group_downloaded_20201015.csv"
cdcList_old_20201015 <- readRunCDCAllCause(loc=cdcLoc,
startYear=2015,
curYear=2020,
weekThru=34,
startWeek=9,
lst=readFromRDS("test_hier5_201001"),
epiMap=readFromRDS("epiMonth"),
agePopData=readFromRDS("usPopBucket2020"),
cvDeathThru="2020-08-22",
cdcPlotStartWeek=10
)## Observations: 176,686
## Variables: 11
## $ Jurisdiction <chr> "Alabama", "Alabama", "Alabama", "Alabama", "A...
## $ `Week Ending Date` <chr> "01/10/2015", "01/17/2015", "01/24/2015", "01/...
## $ `State Abbreviation` <chr> "AL", "AL", "AL", "AL", "AL", "AL", "AL", "AL"...
## $ Year <int> 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015...
## $ Week <int> 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14,...
## $ `Age Group` <chr> "25-44 years", "25-44 years", "25-44 years", "...
## $ `Number of Deaths` <dbl> 67, 49, 55, 59, 47, 59, 41, 47, 59, 57, 54, 50...
## $ `Time Period` <chr> "2015-2019", "2015-2019", "2015-2019", "2015-2...
## $ Type <chr> "Predicted (weighted)", "Predicted (weighted)"...
## $ Suppress <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA...
## $ Note <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA...
## Observations: 176,686
## Variables: 11
## $ Jurisdiction <chr> "Alabama", "Alabama", "Alabama", "Alabama", "Alabama",...
## $ weekEnding <date> 2015-01-10, 2015-01-17, 2015-01-24, 2015-01-31, 2015-...
## $ state <chr> "AL", "AL", "AL", "AL", "AL", "AL", "AL", "AL", "AL", ...
## $ year <int> 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015, ...
## $ week <int> 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16,...
## $ age <chr> "25-44 years", "25-44 years", "25-44 years", "25-44 ye...
## $ deaths <dbl> 67, 49, 55, 59, 47, 59, 41, 47, 59, 57, 54, 50, 58, 42...
## $ period <chr> "2015-2019", "2015-2019", "2015-2019", "2015-2019", "2...
## $ type <chr> "Predicted (weighted)", "Predicted (weighted)", "Predi...
## $ Suppress <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA...
## $ Note <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA...
##
## Check Control Levels and Record Counts for Renamed Data:
## # A tibble: 6 x 4
## age n n_deaths_na deaths
## <chr> <int> <int> <dbl>
## 1 25-44 years 26446 1 3226901
## 2 45-64 years 32314 4 12655629
## 3 65-74 years 32308 4 12546354
## 4 75-84 years 32327 5 15599787
## 5 85 years and older 32314 5 20352070
## 6 Under 25 years 20977 0 1392852
## # A tibble: 12 x 6
## # Groups: period, year [6]
## period year type n n_deaths_na deaths
## <chr> <int> <chr> <int> <int> <dbl>
## 1 2015-2019 2015 Predicted (weighted) 15287 0 5416412
## 2 2015-2019 2015 Unweighted 15287 0 5416412
## 3 2015-2019 2016 Predicted (weighted) 15365 0 5483764
## 4 2015-2019 2016 Unweighted 15365 0 5483764
## 5 2015-2019 2017 Predicted (weighted) 15317 0 5643337
## 6 2015-2019 2017 Unweighted 15317 0 5643337
## 7 2015-2019 2018 Predicted (weighted) 15305 0 5698007
## 8 2015-2019 2018 Unweighted 15305 0 5698007
## 9 2015-2019 2019 Predicted (weighted) 15317 0 5725524
## 10 2015-2019 2019 Unweighted 15317 0 5725524
## 11 2020 2020 Predicted (weighted) 11773 13 4969185
## 12 2020 2020 Unweighted 11731 6 4870320
## # A tibble: 3 x 5
## # Groups: period [2]
## period Suppress n n_deaths_na deaths
## <chr> <chr> <int> <int> <dbl>
## 1 2015-2019 <NA> 153182 0 5.59e7
## 2 2020 Suppressed (counts highly incomplete, <5~ 19 19 0.
## 3 2020 <NA> 23485 0 9.84e6
## # A tibble: 10 x 5
## # Groups: period [2]
## period Note n n_deaths_na deaths
## <chr> <chr> <int> <int> <dbl>
## 1 2015-20~ <NA> 153182 0 5.59e7
## 2 2020 Data in recent weeks are incomplete. Onl~ 18954 0 8.34e6
## 3 2020 Data in recent weeks are incomplete. Onl~ 420 0 1.96e5
## 4 2020 Data in recent weeks are incomplete. Onl~ 394 16 4.47e4
## 5 2020 Data in recent weeks are incomplete. Onl~ 1362 3 2.99e5
## 6 2020 Data in recent weeks are incomplete. Onl~ 12 0 5.54e3
## 7 2020 Estimates for Pennsylvania are too low f~ 36 0 1.69e4
## 8 2020 Weights may be too low to account for un~ 248 0 6.06e4
## 9 2020 Weights may be too low to account for un~ 12 0 5.69e3
## 10 2020 <NA> 2066 0 8.77e5
## state Jurisdiction n n_deaths_na deaths
## 1 US United States 3600 0 32767366
## 2 CA California 3600 0 3119418
## 3 FL Florida 3600 0 2395987
## 4 TX Texas 3600 0 2358271
## 5 PA Pennsylvania 3600 0 1571585
## 6 OH Ohio 3600 0 1416195
## 7 IL Illinois 3600 0 1237033
## 8 NY New York 3600 0 1171620
## 9 MI Michigan 3599 0 1125676
## 10 NC North Carolina 3536 8 1060294
## 11 GA Georgia 3600 0 982315
## 12 NJ New Jersey 3594 0 878338
## 13 TN Tennessee 3600 0 855255
## 14 VA Virginia 3600 0 785789
## 15 IN Indiana 3598 0 760849
## 16 MO Missouri 3597 0 740140
## 17 AZ Arizona 3600 0 694914
## 18 MA Massachusetts 3568 0 694218
## 19 YC New York City 3596 0 678875
## 20 WA Washington 3600 0 655215
## 21 AL Alabama 3596 0 608067
## 22 WI Wisconsin 3581 0 601074
## 23 MD Maryland 3594 0 578852
## 24 SC South Carolina 3598 0 570629
## 25 KY Kentucky 3564 0 554152
## 26 LA Louisiana 3590 0 534961
## 27 MN Minnesota 3557 0 511091
## 28 CO Colorado 3598 0 453533
## 29 OK Oklahoma 3590 0 452162
## 30 OR Oregon 3429 0 419882
## 31 MS Mississippi 3533 0 369852
## 32 AR Arkansas 3497 0 367683
## 33 CT Connecticut 3155 8 361512
## 34 IA Iowa 3232 0 344962
## 35 PR Puerto Rico 3317 0 336978
## 36 KS Kansas 3292 0 301264
## 37 NV Nevada 3339 0 293938
## 38 WV West Virginia 3042 3 253845
## 39 UT Utah 3486 0 217243
## 40 NM New Mexico 3180 0 208428
## 41 NE Nebraska 2886 0 190958
## 42 ME Maine 2686 0 162773
## 43 ID Idaho 2804 0 155435
## 44 NH New Hampshire 2708 0 137002
## 45 HI Hawaii 2596 0 126342
## 46 RI Rhode Island 2513 0 116090
## 47 MT Montana 2592 0 111506
## 48 DE Delaware 2599 0 101066
## 49 SD South Dakota 2485 0 87752
## 50 ND North Dakota 2467 0 76289
## 51 DC District of Columbia 2581 0 65035
## 52 VT Vermont 2370 0 62844
## 53 WY Wyoming 2356 0 48148
## 54 AK Alaska 2385 0 42892
## Observations: 176,686
## Variables: 11
## $ Jurisdiction <chr> "Alabama", "Alabama", "Alabama", "Alabama", "Alabama",...
## $ weekEnding <date> 2015-01-10, 2015-01-17, 2015-01-24, 2015-01-31, 2015-...
## $ state <chr> "AL", "AL", "AL", "AL", "AL", "AL", "AL", "AL", "AL", ...
## $ year <fct> 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015, ...
## $ week <int> 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16,...
## $ age <fct> 25-44 years, 25-44 years, 25-44 years, 25-44 years, 25...
## $ deaths <dbl> 67, 49, 55, 59, 47, 59, 41, 47, 59, 57, 54, 50, 58, 42...
## $ period <fct> 2015-2019, 2015-2019, 2015-2019, 2015-2019, 2015-2019,...
## $ type <chr> "Predicted (weighted)", "Predicted (weighted)", "Predi...
## $ Suppress <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA...
## $ Note <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA...
## Observations: 86,667
## Variables: 11
## $ Jurisdiction <chr> "Alabama", "Alabama", "Alabama", "Alabama", "Alabama",...
## $ weekEnding <date> 2015-01-10, 2015-01-17, 2015-01-24, 2015-01-31, 2015-...
## $ state <chr> "AL", "AL", "AL", "AL", "AL", "AL", "AL", "AL", "AL", ...
## $ year <fct> 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015, ...
## $ week <int> 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16,...
## $ age <fct> 25-44 years, 25-44 years, 25-44 years, 25-44 years, 25...
## $ deaths <dbl> 67, 49, 55, 59, 47, 59, 41, 47, 59, 57, 54, 50, 58, 42...
## $ period <fct> 2015-2019, 2015-2019, 2015-2019, 2015-2019, 2015-2019,...
## $ type <chr> "Predicted (weighted)", "Predicted (weighted)", "Predi...
## $ Suppress <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA...
## $ Note <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA...
##
## Confirm that data suppressions and NA deaths have been aliminated:
## # A tibble: 0 x 11
## # ... with 11 variables: Jurisdiction <chr>, weekEnding <date>, state <chr>,
## # year <fct>, week <int>, age <fct>, deaths <dbl>, period <fct>, type <chr>,
## # Suppress <chr>, Note <chr>
## Observations: 81,511
## Variables: 12
## $ Jurisdiction <chr> "Alabama", "Alabama", "Alabama", "Alabama", "Alabama",...
## $ weekEnding <date> 2015-01-10, 2015-01-10, 2015-01-10, 2015-01-10, 2015-...
## $ state <chr> "AL", "AL", "AL", "AL", "AL", "AL", "AL", "AL", "AL", ...
## $ year <fct> 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015, ...
## $ week <int> 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, ...
## $ age <fct> Under 25 years, 25-44 years, 45-64 years, 65-74 years,...
## $ period <fct> 2015-2019, 2015-2019, 2015-2019, 2015-2019, 2015-2019,...
## $ type <chr> "Predicted (weighted)", "Predicted (weighted)", "Predi...
## $ Suppress <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA...
## $ n <int> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...
## $ deaths <dbl> 25, 67, 253, 202, 272, 320, 28, 49, 256, 222, 253, 332...
## $ Note <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA...
##
## First duplicate is in row number (0 means no duplicates): 0
## Warning: Column `state` joining factor and character vector, coercing into
## character vector
## Joining, by = "state"
## Warning: Removed 1 rows containing missing values (position_stack).## Warning: Removed 1 rows containing missing values (geom_text).
The evolution of the county-level segments from September 3 to October 15 can also be explored. Since the process is run using kmeans, segment numbers have no inherent meaning:
# Previous and current county clusters data
ctyChange <- tibble::tibble(fips1=names(readFromRDS("clustVec_county_20200903_new")),
fips2=names(cty_20201015$clustVec),
cluster1=readFromRDS("clustVec_county_20200903_new"),
cluster2=cty_20201015$clustVec,
mism=(fips1!=fips2),
fips=stringr::str_pad(fips1, side="left", pad="0", width=5)
) %>%
left_join(usmap::countypop, by="fips")
# Control totals
ctyChange %>%
summarize(sumMismatch=sum(mism), sumna=sum(is.na(county)), sumpop=sum(pop_2015))## # A tibble: 1 x 3
## sumMismatch sumna sumpop
## <int> <int> <dbl>
## 1 0 0 301897023# Change in segments
ctyChange %>%
group_by(cluster1, cluster2) %>%
summarize(n=n(), pop=sum(pop_2015)) %>%
ggplot(aes(x=factor(cluster2), y=factor(cluster1))) +
geom_tile(aes(fill=pop)) +
geom_text(aes(label=paste0("n=", n, "\nPop. ", round(pop/1000000, 1), " mln"))) +
scale_fill_continuous("Population", high="lightgreen", low="white") +
labs(x="New Cluster (Oct 15)", y="Old Cluster (Sep 3)", title="Evolution of County Clusters")## `summarise()` regrouping output by 'cluster1' (override with `.groups` argument)
There has been some meaningful evolution of the county-level clusters, particularly for the small cluster with a large disease burden. The totals are examined:
smallCluster <- ctyChange %>%
count(cluster1) %>%
filter(n==min(n)) %>%
pull(cluster1)
segChange <- ctyChange %>%
filter(cluster1==smallCluster) %>%
arrange(cluster2, -pop_2015) %>%
select(-fips1, -fips2, -mism)
segChange %>%
filter(pop_2015 >= 250000) %>%
as.data.frame()## cluster1 cluster2 fips abbr county pop_2015
## 1 3 2 36047 NY Kings County 2636735
## 2 3 2 36081 NY Queens County 2339150
## 3 3 2 26163 MI Wayne County 1759335
## 4 3 2 36061 NY New York County 1644518
## 5 3 2 36005 NY Bronx County 1455444
## 6 3 2 36059 NY Nassau County 1361350
## 7 3 2 36119 NY Westchester County 976396
## 8 3 2 09001 CT Fairfield County 948053
## 9 3 2 34003 NJ Bergen County 938506
## 10 3 2 09003 CT Hartford County 895841
## 11 3 2 34023 NJ Middlesex County 840900
## 12 3 2 34013 NJ Essex County 797434
## 13 3 2 25009 MA Essex County 776043
## 14 3 2 25021 MA Norfolk County 696023
## 15 3 2 34017 NJ Hudson County 674836
## 16 3 2 34029 NJ Ocean County 588721
## 17 3 2 34039 NJ Union County 555786
## 18 3 2 34031 NJ Passaic County 510916
## 19 3 2 25023 MA Plymouth County 510393
## 20 3 2 34027 NJ Morris County 499509
## 21 3 2 36085 NY Richmond County 474558
## 22 3 2 25013 MA Hampden County 470690
## 23 3 2 48061 TX Cameron County 422156
## 24 3 2 22071 LA Orleans Parish 389617
## 25 3 2 34021 NJ Mercer County 371398
## 26 3 2 34035 NJ Somerset County 333654
## 27 3 2 36087 NY Rockland County 326037
## 28 3 3 36103 NY Suffolk County 1501587
## 29 3 3 25025 MA Suffolk County 778121
## 30 3 3 34025 NJ Monmouth County 628715segChange %>%
mutate(cluster2=factor(cluster2)) %>%
filter(pop_2015 >= 100000) %>%
usmap::plot_usmap(regions="counties", data=., values="cluster2") +
scale_fill_discrete("New segment", na.value="white")
cty_20201015$burdenData %>%
select(countyFIPS, date, cumDeaths, population, countyName=county) %>%
mutate(countyFIPS=stringr::str_pad(countyFIPS, side="left", width=5, pad="0")) %>%
inner_join(select(ctyChange, fips, cluster1, cluster2), by=c("countyFIPS"="fips")) %>%
group_by(cluster1, cluster2, date) %>%
summarize(cumDeaths=sum(cumDeaths), population=sum(population)) %>%
ggplot(aes(x=date, y=1000000*cumDeaths/population)) +
geom_line(aes(color=factor(cluster2))) +
facet_grid(paste0("New ", cluster2)~paste0("Old ", cluster1)) +
labs(x="Date",
y="Cumulative Deaths per million",
title="Deaths per million by segment",
subtitle="Old segment (03 SEP) and new segment (15 OCT)"
)## `summarise()` regrouping output by 'cluster1', 'cluster2' (override with `.groups` argument)
The segmenting methodology puts a much higher weight on total burden than on shape of the curve. So, as some of the (mostly) southern counties have had significant burden increases, the segments have been reorganizing to reflect that.
Next steps are to work with the county clusters using a higher weight on shape of the curve. It will be meaningful to have distinct early/high and late/high segments similar to what is seen with the states. The patterns appear potentially similar to the states:
- Early and high
- Early and moderate
- Late and high
- Late and moderate
- Persistently moderate
- Persistently low
The county clustering process is updated to allow for hierarchical clustering and a more rules-based clustering based on simple heuristics (high, medium, low; early, sustained, late):
# Function to run the USA Facts (US county-level coronavirus data) clustering process
readRunUSAFacts <- function(maxDate,
popLoc,
caseLoc,
deathLoc,
dlPop=FALSE,
dlCaseDeath=FALSE,
ovrWrite=FALSE,
ovrWriteError=TRUE,
oldFile=NULL,
showBurdenMinPop=10000,
minPopCluster=25000,
existingStateClusters=NULL,
existingCountyClusters=NULL,
createClusters=FALSE,
hierarchical=FALSE,
kCut=6,
orderCluster=TRUE,
...
) {
# FUNCTION ARGUMENTS:
# maxDate: the maximum data to use for data from the cases and deaths file
# popLoc: location where the county-level population data are stored
# caseLoc: location where the county-level cases data are stored
# deathLoc: location where the county-level deaths data are stored
# dlPop: boolean, should new population data be downloaded to popLoc
# dlCaseDeath: boolean, should new case data and death data be downloaded to caseLoc and deathLoc
# ovrWrite: boolean, if data are downloaded to an existing file, should it be over-written
# ovrWriteError: boolean, if ovrWrite is FALSE and an attempt to overwrite is made, should it error out?
# oldFile: old file for comparing metrics against (NULL means no old file for comarisons)
# showBurdenMinPop: minimum population for showing in burden by cluster plots (NULL means skip plot)
# minPopCluster: minimum population for including county in running cluster-level metrics
# existingStateClusters: location of an existing named vector with clusters by state (NULL means none)
# existingCountyClusters: location of an existing named vector with clusters by county (NULL means none)
# if existingStateClusters is not NULL, then existingCountyClusters is ignored
# createClusters: boolean, whether to create new clusters (only set up for kmeans)
# hierarchical: whether to create hierarchical clusters
# TRUE means run hierarchical clustering
# FALSE means run kmeans clustering
# NA means run rules-based clustering
# kCut; if hierarchical clustering is used, what k (number of clusters in cutree) should be used?
# orderCluster: if FALSE, ignore; if TRUE, order by "dpm"; if anything else, order by orderCluster
# ...: other arguments that will be passed to prepClusterCounties
# STEP 0: Download new files (if requested)
urlCase <- "https://usafactsstatic.blob.core.windows.net/public/data/covid-19/covid_confirmed_usafacts.csv"
urlDeath <- "https://usafactsstatic.blob.core.windows.net/public/data/covid-19/covid_deaths_usafacts.csv"
urlPop <- "https://usafactsstatic.blob.core.windows.net/public/data/covid-19/covid_county_population_usafacts.csv"
# Helper function to download a file
helperDownload <- function(url, loc, ovrWrite=ovrWrite, ovrWriteError=ovrWriteError) {
# If the file exists, mention it and proceed as per the guard checks
if (file.exists(loc)) {
cat("\nFile:", loc, "already exists\n")
if (!ovrWrite & ovrWriteError) stop("\nExiting due to ovrWrite=FALSE and ovrWriteError=TRUE\n")
if (!ovrWrite & !ovrWriteError) {
cat("\nFile is NOT downloaded again\n")
return(NULL)
}
}
# Download the file and change to read-only
download.file(url, destfile=loc, method="curl")
Sys.chmod(loc, mode="0555", use_umask = FALSE)
}
if (dlPop) helperDownload(urlPop, loc=popLoc)
if (dlCaseDeath) helperDownload(urlCase, loc=caseLoc)
if (dlCaseDeath) helperDownload(urlDeath, loc=deathLoc)
# STEP 1: Read in the population file
pop <- readr::read_csv(popLoc) %>%
rename(countyName=`County Name`, state=State)
# STEP 2: Read case and death data, combine, and add population totals and existing clusters
burdenData <- readUSAFacts(
caseFile=caseLoc,
deathFile=deathLoc,
countyPopFile=pop,
oldFile=oldFile,
showBurdenMinPop=showBurdenMinPop,
maxDate=maxDate,
stateClusters=existingStateClusters,
countyClusters=existingCountyClusters,
glimpseRaw=FALSE
)
# STEP 3: Create appropriately filtered data, and new clusters if requested
clusterData <- prepClusterCounties(burdenFile=burdenData,
maxDate=maxDate,
minPop=minPopCluster,
createClusters=createClusters,
hierarchical=hierarchical,
returnList=TRUE,
...
)
# STEP 4: Assess clusters against the new data
# STEP 4a: Extract the county-level clusters (new clusters if created, existing otherwise)
if (createClusters) {
if (is.na(hierarchical)) clustVec <- clusterData$objCluster$objCluster
else if (hierarchical) clustVec <- cutree(clusterData$objCluster$objCluster, k=kCut)
else clustVec <- clusterData$objCluster$objCluster$cluster
}
else {
clustVec <- existingCountyClusters
}
# STEP 4b: Show the cumulative data, order by cluster, and keep the plots together
helperACC_county <- helperAssessCountyClusters(vecCluster=clustVec,
dfPop=clusterData$countyFiltered,
dfBurden=clusterData$countyFiltered,
showCum=TRUE,
thruLabel=format(as.Date(maxDate), "%b %d, %Y"),
plotsTogether=TRUE,
orderCluster=orderCluster
)
# STEP 5: Add back clusters not used for analysis (code 999) and associated disease data
# May want to change the approach to population data
clusterStateData <- helperMakeClusterStateData(dfPlot=helperACC_county,
dfPop=usmap::countypop,
dfBurden=clusterData$countyDailyPerCapita,
orderCluster=orderCluster
)
# STEP 6: Return a list of the key files
list(pop=pop,
burdenData=burdenData,
clusterData=clusterData,
clustVec=clustVec,
helperACC_county=helperACC_county,
clusterStateData=clusterStateData,
maxDate=maxDate
)
}
# STEP 4: Create county-level clusters
prepClusterCounties <- function(burdenFile,
maxDate,
minPop,
createClusters=TRUE,
hierarchical=FALSE,
returnList=!hierarchical,
...
) {
# FUNCTION ARGUMENTS:
# burdenFile: the pivoted file containing the burdens data
# maxDate: the latest date to be used from the data
# minPop: the smallest population for a county to be included
# createClusters: boolean, whether to create county-level clusters (if FALSE, object returned as NULL)
# FALSE would be for prepping and converting data only (those objects are returned)
# hierarchical: whether to create hierarchical clusters
# TRUE means run hierarchical clustering
# FALSE means run kmeans clustering
# NA means run rules-based clustering
# returnList: whether the clustering call returns a list (only set up for TRUE as of now)
# ...: other arguments to be passed to clusterStates
# STEP 1: Select only desired variables from burden file
countyCumPerCapita <- burdenFile %>%
select(state=countyFIPS, date, cpm=cumCasesPer, dpm=cumDeathPer, population) %>%
arrange(state, date)
# STEP 2: Confirm that there are no duplicates and that every county has the same dates
# This should be 1 provided that there are no duplicates
countyCumPerCapita %>%
count(state, date) %>%
pull(n) %>%
max()
# This should have no standard deviation if the same number of records exist on every day
countyCumPerCapita %>%
mutate(n=1) %>%
group_by(date) %>%
summarize(n=sum(n), population=sum(population)) %>%
summarize_at(vars(all_of(c("n", "population"))), .funs=list(sd=sd, min=min, max=max))
# STEP 3: Convert to daily new totals rather than cumulative data
countyDailyPerCapita <- countyCumPerCapita %>%
group_by(state) %>%
arrange(date) %>%
mutate_at(vars(all_of(c("cpm", "dpm"))), ~ifelse(row_number()==1, ., .-lag(.))) %>%
ungroup()
# STEP 4: Add rolling 7 aggregates and total cases/deaths
countyDailyPerCapita <- countyDailyPerCapita %>%
arrange(state, date) %>%
group_by(state) %>%
helperRollingAgg(origVar="cpm", newName="cpm7", k=7) %>%
helperRollingAgg(origVar="dpm", newName="dpm7", k=7) %>%
ungroup() %>%
mutate(cases=cpm*population/1000000, deaths=dpm*population/1000000)
# STEP 5: Filter the data prior to clustering
countyFiltered <- countyDailyPerCapita %>%
filter(population >= minPop, date <= as.Date(maxDate)) %>%
mutate(state=as.character(state))
# STEP 6: Check the implications of the filtering
# Check number of counties that will fail the test for 100 deaths per million or 5000 cases per million
is0 <- function(x) mean(x==0)
isltn <- function(x, n) mean(x<n)
islt100 <- function(x) isltn(x, n=100)
islt5000 <- function(x) isltn(x, n=5000)
countyFiltered %>%
group_by(state) %>%
summarize_at(c("cpm", "dpm"), sum) %>%
ungroup() %>%
summarize_at(vars(all_of(c("cpm", "dpm"))),
.funs=list(mean_is0=is0, mean_lt100=islt100, mean_lt5000=islt5000)
) %>%
print()
# Run county-level clusters if requested, otherwise store as NULL
objCluster <- if(createClusters) {
clusterStates(countyFiltered, hierarchical=hierarchical, returnList=returnList, ...)
} else {
NULL
}
# Return all of the relevant objects
list(objCluster=objCluster,
countyFiltered=countyFiltered,
countyDailyPerCapita=countyDailyPerCapita,
countyCumPerCapita=countyCumPerCapita
)
}
# Function to create clusters for the state data (requires all data from same year, as currently true)
clusterStates <- function(df,
caseVar="cpm",
deathVar="dpm",
shapeFunc=lubridate::month,
minShape=NULL,
maxShape=NULL,
minDeath=0,
maxDeath=Inf,
minCase=0,
maxCase=Inf,
ratioTotalvsShape=1,
ratioDeathvsCase=1,
hierarchical=TRUE,
hierMethod="complete",
nCenters=3,
iter.max=10,
nstart=1,
testCenters=NULL,
returnList=FALSE,
seed=NULL
) {
# FUNCTION ARGUMENTS:
# df: the data frame containing cases and deaths data
# caseVar: the variable containing the cases per capita data
# deathVar: the variable containing the deaths per capita data
# shapeFunc: the function to be used for creating the shape of the curve
# minShape: the minimum value to be used for shape (to avoid very small amounts of data in Jan/Feb/Mar)
# shape is the month, so 4 means start with April data (NULL means keep everything)
# maxShape: the maximum value to be used for shape (to avoid very small amounts of data in a partial month)
# shape is the month, so 9 means end with September data (NULL means keep everything)
# minDeath: use this value as a floor for the death metric when calculating shape
# maxDeath: use this value as a maximum when calculating distance using deaths
# minCase: use this value as a floor for the case metric when calculating shape
# maxCase: use this value as a maximum when calculating distance using cases
# ratioTotalvsShape: amount of standard deviation to be kept in total variable vs shape variables
# ratioDeathvsCase: amount of standard deviation to be kept in deaths vs cases
# (total death data will be scaled to have sd this many times higher than cases)
# (death percentages by time period will be scaled directly by this amount)
# hierarchical: whether to create hierarchical clusters
# TRUE means run hierarchical clustering
# FALSE means run kmeans clustering
# NA means run rules-based clustering
# hierMethod: the method for hierarchical clustering (e.g., 'complete' or 'single')
# nCenters: the number of centers to use for kmeans clustering
# testCenters: integer vector of centers to test (will create an elbow plot); NULL means do not test
# iter.max: maximumum number of kmeans iterations (default in kmeans algorithm is 10)
# nstart: number of random sets chosen for kmeans (default in kmeans algorithm is 1)
# returnList: boolean, if FALSE just the cluster object is returned
# if TRUE, a list is returned with dfCluster and the cluster object
# seed: set the seed to this value (NULL means no seed)
# Extract key information (aggregates and by shapeFunc for each state)
df <- df %>%
select_at(vars(all_of(c("date", "state", caseVar, deathVar)))) %>%
purrr::set_names(c("date", "state", "cases", "deaths")) %>%
mutate(timeBucket=shapeFunc(date)) %>%
group_by(state, timeBucket) %>%
summarize(cases=sum(cases), deaths=sum(deaths)) %>%
ungroup()
# Limit to only relevant time buckets if requested
if (!is.null(minShape)) {
df <- df %>%
filter(timeBucket >= minShape)
}
if (!is.null(maxShape)) {
df <- df %>%
filter(timeBucket <= maxShape)
}
# Extract an aggregate by state, scaled so that they have the proper ratio
dfAgg <- df %>%
group_by(state) %>%
summarize(totalCases=sum(cases), totalDeaths=sum(deaths)) %>%
mutate(totalCases=pmin(totalCases, maxCase), totalDeaths=pmin(totalDeaths, maxDeath)) %>%
ungroup() %>%
mutate(totalDeaths=ratioDeathvsCase*totalDeaths*sd(totalCases)/sd(totalDeaths))
# Extract the percentages (shapes) by month, scaled so that they have the proper ratio
dfShape <- df %>%
pivot_longer(-c(state, timeBucket)) %>%
group_by(state, name) %>%
mutate(tot=pmax(sum(value), ifelse(name=="deaths", minDeath, minCase)),
value=ifelse(name=="deaths", ratioDeathvsCase, 1) * value / tot) %>%
select(-tot) %>%
pivot_wider(state, names_from=c(name, timeBucket), values_from=value) %>%
ungroup()
# Function to calculate SD of a subset of columns
calcSumSD <- function(df) {
df %>%
ungroup() %>%
select(-state) %>%
summarize_all(.funs=sd) %>%
as.vector() %>%
sum()
}
# Down-weight the aggregate data so that there is the proper sum of sd in aggregates and shapes
aggSD <- calcSumSD(dfAgg)
shapeSD <- calcSumSD(dfShape)
dfAgg <- dfAgg %>%
mutate_if(is.numeric, ~. * ratioTotalvsShape * shapeSD / aggSD)
# Combine so there is one row per state
dfCluster <- dfAgg %>%
inner_join(dfShape, by="state")
# convert 'state' to rowname
keyData <- dfCluster %>% column_to_rownames("state")
# Create hierarchical segments or kmeans segments
if (is.na(hierarchical)) {
# Create pseudo-rules-based segments
if (!is.null(seed)) set.seed(seed)
# STEP 1: Classify high-medium-low based on deaths and cases
hml <- kmeans(select(keyData, starts_with("total")), centers=3)
# STEP 2: Classify early-late based on shape
esl <- kmeans(select(keyData, -starts_with("total")), centers=2)
# STEP 3: Create a final segment
objCluster <- 2*(hml$cluster-1) + esl$cluster
} else if (hierarchical) {
# Create hierarchical segments
objCluster <- hclust(dist(keyData), method=hierMethod)
plot(objCluster)
} else {
# Create k-means segments
# Create an elbow plot if testCenters is not NULL
if (!is.null(testCenters)) {
helperElbow(keyData, testCenters=testCenters, iter.max=iter.max, nstart=nstart, silhouette=TRUE)
}
# Create the kmeans cluster object, setting a seed if requested
if (!is.null(seed)) set.seed(seed)
objCluster <- kmeans(keyData, centers=nCenters, iter.max=iter.max, nstart=nstart)
cat("\nCluster means and counts\n")
n=objCluster$size %>% cbind(objCluster$centers) %>% round(2) %>% t() %>% print()
}
# Return the data and object is a list if returnList is TRUE, otherwise return only the clustering object
if (!isTRUE(returnList)) {
objCluster
} else {
list(objCluster=objCluster, dfCluster=dfCluster)
}
}
# Create segments and download data from USA Facts
caseLoc <- "./RInputFiles/Coronavirus/covid_confirmed_usafacts_downloaded_20201015.csv"
deathLoc <- "./RInputFiles/Coronavirus/covid_deaths_usafacts_downloaded_20201015.csv"
# Create 3x2 clusters for hml and shape
cty_20201015_rule6 <- readRunUSAFacts(maxDate="2020-10-13",
popLoc="./RInputFiles/Coronavirus/covid_county_population_usafacts.csv",
caseLoc=caseLoc,
deathLoc=deathLoc,
dlCaseDeath=!(file.exists(caseLoc) & file.exists(deathLoc)),
oldFile=readFromRDS("burden_20200903_new"),
existingStateClusters=test_hier6_201014$useClusters,
createClusters=TRUE,
hierarchical=NA,
minShape=3,
ratioDeathvsCase = 5,
ratioTotalvsShape = 0.25,
minDeath=100,
minCase=5000
)##
## -- Column specification --------------------------------------------------------
## cols(
## countyFIPS = col_double(),
## `County Name` = col_character(),
## State = col_character(),
## population = col_double()
## )##
## -- Column specification --------------------------------------------------------
## cols(
## .default = col_double(),
## `County Name` = col_character(),
## State = col_character()
## )
## i Use `spec()` for the full column specifications.## Rows: 849,870
## Columns: 6
## $ countyFIPS <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ countyName <chr> "Statewide Unallocated", "Statewide Unallocated", "State...
## $ state <chr> "AL", "AL", "AL", "AL", "AL", "AL", "AL", "AL", "AL", "A...
## $ stateFIPS <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,...
## $ date <date> 2020-01-22, 2020-01-23, 2020-01-24, 2020-01-25, 2020-01...
## $ cumCases <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...## Warning: `expand_scale()` is deprecated; use `expansion()` instead.##
## -- Column specification --------------------------------------------------------
## cols(
## .default = col_double(),
## `County Name` = col_character(),
## State = col_character()
## )
## i Use `spec()` for the full column specifications.## Rows: 849,870
## Columns: 6
## $ countyFIPS <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ countyName <chr> "Statewide Unallocated", "Statewide Unallocated", "State...
## $ state <chr> "AL", "AL", "AL", "AL", "AL", "AL", "AL", "AL", "AL", "A...
## $ stateFIPS <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,...
## $ date <date> 2020-01-22, 2020-01-23, 2020-01-24, 2020-01-25, 2020-01...
## $ cumDeaths <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...## Warning: `expand_scale()` is deprecated; use `expansion()` instead.## `summarise()` regrouping output by 'source' (override with `.groups` argument)
## `geom_smooth()` using formula 'y ~ x'## `summarise()` ungrouping output (override with `.groups` argument)
## `summarise()` ungrouping output (override with `.groups` argument)## # A tibble: 1 x 6
## cpm_mean_is0 dpm_mean_is0 cpm_mean_lt100 dpm_mean_lt100 cpm_mean_lt5000
## <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 0 0.0157 0 0.147 0.0559
## # ... with 1 more variable: dpm_mean_lt5000 <dbl>## `summarise()` regrouping output by 'state' (override with `.groups` argument)
## `summarise()` ungrouping output (override with `.groups` argument)
## `summarise()` ungrouping output (override with `.groups` argument)## `summarise()` regrouping output by 'cluster' (override with `.groups` argument)## `summarise()` ungrouping output (override with `.groups` argument)
## `summarise()` ungrouping output (override with `.groups` argument)## `summarise()` regrouping output by 'date', 'cluster' (override with `.groups` argument)## `summarise()` ungrouping output (override with `.groups` argument)
##
## Recency is defined as 2020-09-14 through current
##
## Recency is defined as 2020-09-14 through current## Warning: Removed 1 rows containing missing values (geom_point).## `summarise()` regrouping output by 'cluster' (override with `.groups` argument)## `summarise()` regrouping output by 'cluster' (override with `.groups` argument)
## `summarise()` regrouping output by 'cluster' (override with `.groups` argument)
## `summarise()` regrouping output by 'cluster' (override with `.groups` argument)
## Warning: `expand_scale()` is deprecated; use `expansion()` instead.
## Joining, by = "fipsCounty"## Joining, by = "fipsCounty"## `summarise()` regrouping output by 'cluster' (override with `.groups` argument)## `summarise()` ungrouping output (override with `.groups` argument)
At a glance, the rule-based approach creates reasonable county-level segments that are generally well differentiated as far as burden and shape. Next steps are to continue to explore the county-level segmenting approach.
The rules-based approach pulls apart differences in both shape and magnitude, focused mainly on coronavirus deaths. The cases data are less reliable given the significant variation in testing by locale and timing.
Suppose that three archetype deaths per million by month are examined:
# Archetype curves for deaths by month
archeDeaths <- tibble::tibble(mon=factor(rep(month.abb[3:9], times=3), levels=month.abb),
lvl=rep(letters[1:3], each=7),
dpm=c(0, 250, 750, 500, 250, 100, 50,
0, 25, 200, 200, 125, 50, 50,
0, 0, 50, 50, 50, 150, 200
)
)
# Plot of dpm by archetype
p1 <- archeDeaths %>%
ggplot(aes(x=mon, y=dpm)) +
geom_line(aes(group=lvl, color=lvl)) +
labs(x="", y="Deaths per million", title="Simulated archetype deaths per million") +
scale_color_discrete("Archetype")
p1
p1 + facet_wrap(~lvl, scales="free_y") + labs(subtitle="Each facet is on its own scale")
The rules based segmentation will pull these apart. Archetype a is high and early, archetype b is medium and early, archetype c is medium and late. Any reasonable distance metric will likely separate a and c, but a further objective is to have b (very) roughly equidistant from both a and c. The distance from a is driven by total volume and the distance from c is driven by shape.
Suppose that standard euclidean distance is calculated with the burden by month completely unscaled:
archeDeaths %>%
pivot_wider(lvl, names_from=mon, values_from=dpm) %>%
column_to_rownames("lvl") %>%
dist(method="euclidean", upper=TRUE)## a b c
## a 679.1539 905.5385
## b 679.1539 289.3959
## c 905.5385 289.3959As expected, a and c are the most distant from each other, but b is twice as close to c as to a based on similarity in disease burden. This may tend to drive very different shapes in to the same segment, forcing the segmentation to be “too similar” to just a ranking by total deaths.
Suppose instead that a log transformation is applied to the deaths by month:
archeDeaths %>%
mutate(logdpm=log10(pmax(dpm, 0)+1)) %>%
pivot_wider(lvl, names_from=mon, values_from=logdpm) %>%
column_to_rownames("lvl") %>%
dist(method="euclidean", upper=TRUE)## a b c
## a 1.277613 2.995279
## b 1.277613 1.855526
## c 2.995279 1.855526This approach is encouraging and can be applied to the rules-based segmentation data:
ruleSegData <- cty_20201015_rule6$clusterStateData %>%
filter(!is.na(date)) %>%
mutate(mon=factor(month.abb[mon=lubridate::month(date)], levels=month.abb)) %>%
group_by(fipsCounty, cluster, mon) %>%
summarize(deaths=sum(deaths), pop=mean(pop)) %>%
group_by(cluster, mon) %>%
summarize(deaths=sum(deaths), pop=sum(pop)) %>%
ungroup() %>%
mutate(dpm=1000000*deaths/pop) %>%
filter(mon %in% month.abb[3:9])## `summarise()` regrouping output by 'fipsCounty', 'cluster' (override with `.groups` argument)## `summarise()` regrouping output by 'cluster' (override with `.groups` argument)p2 <- ruleSegData %>%
ggplot(aes(x=mon, y=dpm)) +
geom_line(aes(group=cluster, color=cluster)) +
labs(x="", y="Deaths per million", title="Deather per million by cluster") +
scale_color_discrete("Cluster")
p2
p2 + facet_wrap(~cluster, scales="free_y") + labs(subtitle="Each facet is on its own scale")
segDist <- ruleSegData %>%
mutate(logdpm=log10(1+pmax(0, dpm))) %>%
pivot_wider(cluster, names_from=mon, values_from="logdpm") %>%
column_to_rownames("cluster") %>%
dist(method="euclidean", upper=TRUE, diag=TRUE) %>% as.matrix() %>% as.data.frame() %>%
rownames_to_column("clusterA") %>% pivot_longer(-clusterA, names_to="clusterB")
segDist %>%
filter(clusterA != clusterB, clusterA != 999, clusterB != 999) %>%
ggplot(aes(x=clusterA, y=clusterB)) +
geom_tile(aes(fill=value)) +
geom_text(aes(label=round(value, 1))) +
scale_fill_continuous(high="red", low="white", "Distance") +
labs(x="",
y="",
title="Euclidean distance between cluster averages",
subtitle="Calculated using log10 deaths per million by month"
)
The distances seem sensible, with the methodology assigning high distance when either magnitude OR shape is different. Suppose the methodology is applied to all counties that are not “too small” (classified as 999):
testSegData <- cty_20201015_rule6$clusterStateData %>%
filter(!is.na(date), cluster != 999) %>%
mutate(mon=factor(month.abb[mon=lubridate::month(date)], levels=month.abb)) %>%
group_by(fipsCounty, cluster, mon) %>%
summarize(deaths=sum(deaths), pop=mean(pop)) %>%
ungroup() %>%
mutate(dpm=1000000*deaths/pop, logdpm=log10(1+pmax(0, dpm))) %>%
filter(mon %in% month.abb[3:9])## `summarise()` regrouping output by 'fipsCounty', 'cluster' (override with `.groups` argument)testDistData <- testSegData %>%
pivot_wider(fipsCounty, names_from=mon, values_from=logdpm) %>%
column_to_rownames("fipsCounty") %>%
dist()
testHierClust <- hclust(testDistData, method="complete")
plot(testHierClust)
The dendrogram is encouraging, with an early split in to three, each followed by a split in two. Suppose that 6 segments are created from this:
testClustVec <- cutree(testHierClust, k=6)
testSegOut <- testSegData %>%
mutate(newCluster=testClustVec[fipsCounty])
testSegOut %>%
group_by(fipsCounty, cluster, newCluster) %>%
summarize(deaths=sum(deaths), pop=mean(pop)) %>%
group_by(newCluster, cluster) %>%
summarize(n=n(), pop=sum(pop), deaths=sum(deaths)) %>%
ggplot(aes(x=factor(newCluster), y=cluster)) +
geom_tile(aes(fill=pop/1000000)) +
geom_text(aes(label=paste0(round(pop/1000000, 1), " million\n(n=", n, ")"))) +
scale_fill_continuous("Pop (millions)", high="lightgreen", low="white") +
labs(x="New cluster", y="Rules-based cluster", title="Change in cluster by methodology")## `summarise()` regrouping output by 'fipsCounty', 'cluster' (override with `.groups` argument)## `summarise()` regrouping output by 'newCluster' (override with `.groups` argument)
testSegOut %>%
group_by(mon, cluster, newCluster) %>%
summarize(deaths=sum(deaths), pop=sum(pop), n=n()) %>%
ungroup() %>%
mutate(dpm=1000000*deaths/pop, newCluster=factor(newCluster)) %>%
ggplot(aes(x=mon, y=dpm)) +
geom_line(aes(color=newCluster, group=newCluster)) +
geom_text(aes(x="Jun", y=750, label=paste0("n=", n))) +
facet_grid(newCluster ~ cluster) +
labs(x="Rules-based cluster", y="New cluster", title="Deaths per million by cluster") +
scale_color_discrete("New cluster")## `summarise()` regrouping output by 'mon', 'cluster' (override with `.groups` argument)
Further exploration is needed to understand the driver behind splitting some observations that, at a glance, look very similar.
The issue with the log10 approach is that zero and small start to become better differentiated than small and large (e.g., 0 deaths per million becomes 0, 100 deaths per million becomes 2, and 1000 deaths per million becomes 3). So, the goal of reducing spurious differentiation among outliers on the high end (2000 is 3.3., 4000 is 3.6) has been achieved, but at the cost of losing the power to appropriately separate high, medium, and low.
An additional approach could take advantage of the timing of deaths in the national curve (using only the counties that meet the population elgibility criteria:
ruleNationData <- cty_20201015_rule6$clusterStateData %>%
filter(!is.na(date)) %>%
mutate(mon=factor(month.abb[mon=lubridate::month(date)], levels=month.abb)) %>%
group_by(cluster, date) %>%
summarize(deaths=sum(deaths), pop=sum(pop)) %>%
mutate(dpm=1000000*deaths/pop)## `summarise()` regrouping output by 'cluster' (override with `.groups` argument)ruleNationData %>%
filter(cluster!=999) %>%
ggplot(aes(x=date, y=dpm)) +
geom_line() +
geom_smooth(color="red", span=0.25, method="loess") +
labs(x="",
y="Deaths per million",
title="US coronavirus deaths per million by day",
subtitle="Caution that each facet has its own y scale"
) +
scale_x_date(date_breaks="1 month", date_labels="%b") +
facet_wrap(~cluster, scales="free_y")## `geom_smooth()` using formula 'y ~ x'
ruleNationData %>%
filter(cluster!=999) %>%
mutate(mon=factor(month.abb[lubridate::month(date)], levels=month.abb)) %>%
group_by(cluster, mon) %>%
summarize(deaths=sum(deaths), pop=mean(pop)) %>%
mutate(dpm=1000000*deaths/pop) %>%
ggplot(aes(x=mon, y=dpm)) +
geom_line(aes(group=cluster, color=cluster)) +
labs(x="",
y="Deaths per million",
title="US coronavirus deaths per million by month",
subtitle="Caution that each facet has its own y scale and October is only a partial month"
) +
facet_wrap(~cluster, scales="free_y")## `summarise()` regrouping output by 'cluster' (override with `.groups` argument)
The rule-based segments are nicely differentiated visually, both as far as shape and maximum y-axis value.
There is no meaningful spike in coronavirus until April and October is an incomplete month. Perhaps distance metrics would better be calculated using:
- April and earlier
- May-June
- July-August
- September and later
This avoids some of the spurious zero points that emerge from treating each month in each county as a separate observation:
ruleNationData %>%
filter(cluster!=999) %>%
mutate(mon=factor(month.abb[lubridate::month(date)], levels=month.abb),
monGroup=case_when(mon %in% c("Jan", "Feb", "Mar", "Apr") ~ "thru Apr",
mon %in% c("May", "Jun") ~ "May-Jun",
mon %in% c("Jul", "Aug") ~ "Jul-Aug",
mon %in% c("Sep", "Oct") ~ "Sep and later",
TRUE ~ "Map error"
),
monGroup=factor(monGroup, levels=c("thru Apr", "May-Jun", "Jul-Aug", "Sep and later", "Map error"))
) %>%
group_by(cluster, monGroup) %>%
summarize(deaths=sum(deaths), pop=mean(pop)) %>%
mutate(dpm=1000000*deaths/pop) %>%
ggplot(aes(x=monGroup, y=dpm)) +
geom_line(aes(group=cluster, color=cluster)) +
labs(x="",
y="Deaths per million",
title="US coronavirus deaths per million by month",
subtitle="Caution that each facet has its own y scale and October is only a partial month"
) +
ylim(c(0, NA)) +
facet_wrap(~cluster, scales="free_y")## `summarise()` regrouping output by 'cluster' (override with `.groups` argument)
There are now three distinct shapes of the curve:
- Peak in Jul-Aug (one at ~800 dpm, one at ~400 dpm, one at ~100 dpm)
- Peak in May-Jun (one at ~400 dpm, one at ~100 dpm)
- Peak in thru Apr (one at ~1000 dpm)
Arguably, the rules-based segmenting approach outperforms attempts to calculate a single distance metric between the counties. The risk is that there may be underlying patterns in the data that are not detected by a pre-determined set of rules.
Suppose that the CI for the individual counties is also plotted on the same chart:
allCountyRules <- cty_20201015_rule6$clusterStateData %>%
filter(!is.na(date)) %>%
mutate(mon=factor(month.abb[mon=lubridate::month(date)], levels=month.abb),
monGroup=case_when(mon %in% c("Jan", "Feb", "Mar", "Apr") ~ "thru Apr",
mon %in% c("May", "Jun") ~ "May-Jun",
mon %in% c("Jul", "Aug") ~ "Jul-Aug",
mon %in% c("Sep", "Oct") ~ "Sep and later",
TRUE ~ "Map error"
),
monGroup=factor(monGroup, levels=c("thru Apr", "May-Jun", "Jul-Aug", "Sep and later", "Map error"))
) %>%
group_by(fipsCounty, countyName, state, cluster, monGroup) %>%
summarize(deaths=sum(deaths), pop=mean(pop)) %>%
ungroup() %>%
rbind(mutate(., state="All", countyName="All", fipsCounty="All")) %>%
group_by(fipsCounty, countyName, state, cluster, monGroup) %>%
summarize(deaths=sum(deaths), pop=sum(pop)) %>%
mutate(dpm=1000000*deaths/pop, moddpm=dpm*ifelse(monGroup=="Sep and later", 61/43, 1)) %>%
ungroup()## `summarise()` regrouping output by 'fipsCounty', 'countyName', 'state', 'cluster' (override with `.groups` argument)
## `summarise()` regrouping output by 'fipsCounty', 'countyName', 'state', 'cluster' (override with `.groups` argument)ciData <- allCountyRules %>%
filter(fipsCounty != "All") %>%
group_by(cluster, monGroup) %>%
summarize(ci05=quantile(moddpm, 0.05),
ci25=quantile(moddpm, 0.25),
ci75=quantile(moddpm, 0.75),
ci95=quantile(moddpm, 0.95)
) %>%
ungroup()## `summarise()` regrouping output by 'cluster' (override with `.groups` argument)allCountyRules %>%
ggplot(aes(x=monGroup)) +
geom_line(data=~filter(., fipsCounty=="All"), aes(y=moddpm, group=cluster, color=cluster), lwd=2) +
geom_ribbon(data=ciData, aes(ymin=ci25, ymax=ci75, group=cluster), alpha=0.5) +
geom_ribbon(data=ciData, aes(ymin=ci05, ymax=ci95, group=cluster), fill="grey", alpha=0.5) +
labs(x="",
y="Deaths per million (thru Oct 13*)",
title="US coronavirus deaths per million tracked by county",
subtitle="All facets on same y scale\nDark ribbon covers 50% of counties, light ribbon covers 90%",
caption="* Data through Oct 13 ('Sep and later' scaled by 61/43 to account for shorter time period)"
) +
ylim(c(0, NA)) +
facet_wrap(~cluster)
Tentatively, the analysis will continue with the rules-based segmentation approach to county clusters since it seems to be driving meaningful differentiation for further analysis.
Suppose that evolution in cases is assessed using the county-level clusters:
allCountyRules <- cty_20201015_rule6$clusterStateData %>%
filter(!is.na(date)) %>%
mutate(mon=factor(month.abb[mon=lubridate::month(date)], levels=month.abb),
monGroup=case_when(mon %in% c("Jan", "Feb", "Mar", "Apr") ~ "thru Apr",
mon %in% c("May", "Jun") ~ "May-Jun",
mon %in% c("Jul", "Aug") ~ "Jul-Aug",
mon %in% c("Sep", "Oct") ~ "Sep and later",
TRUE ~ "Map error"
),
monGroup=factor(monGroup, levels=c("thru Apr", "May-Jun", "Jul-Aug", "Sep and later", "Map error"))
) %>%
group_by(fipsCounty, countyName, state, cluster, monGroup) %>%
summarize(deaths=sum(deaths), cases=sum(cases), pop=mean(pop)) %>%
ungroup() %>%
rbind(mutate(., state="All", countyName="All", fipsCounty="All")) %>%
group_by(fipsCounty, countyName, state, cluster, monGroup) %>%
summarize(deaths=sum(deaths), cases=sum(cases), pop=sum(pop)) %>%
mutate(dpm=1000000*deaths/pop,
moddpm=dpm*ifelse(monGroup=="Sep and later", 61/43, 1),
cpm=1000000*cases/pop,
modcpm=cpm*ifelse(monGroup=="Sep and later", 61/43, 1)
) %>%
ungroup()## `summarise()` regrouping output by 'fipsCounty', 'countyName', 'state', 'cluster' (override with `.groups` argument)
## `summarise()` regrouping output by 'fipsCounty', 'countyName', 'state', 'cluster' (override with `.groups` argument)ciDataCases <- allCountyRules %>%
filter(fipsCounty != "All") %>%
group_by(cluster, monGroup) %>%
summarize(ci05=quantile(modcpm, 0.05),
ci25=quantile(modcpm, 0.25),
ci75=quantile(modcpm, 0.75),
ci95=quantile(modcpm, 0.95)
) %>%
ungroup()## `summarise()` regrouping output by 'cluster' (override with `.groups` argument)allCountyRules %>%
ggplot(aes(x=monGroup)) +
geom_line(data=~filter(., fipsCounty=="All"), aes(y=modcpm, group=cluster, color=cluster), lwd=2) +
geom_ribbon(data=ciDataCases, aes(ymin=ci25, ymax=ci75, group=cluster), alpha=0.5) +
geom_ribbon(data=ciDataCases, aes(ymin=ci05, ymax=ci95, group=cluster), fill="grey", alpha=0.5) +
labs(x="",
y="Cases per million (thru Oct 13*)",
title="US coronavirus cases per million tracked by county",
subtitle="All facets on same y scale\nDark ribbon covers 50% of counties, light ribbon covers 90%",
caption="* Data through Oct 13 ('Sep and later' scaled by 61/43 to account for shorter time period)"
) +
ylim(c(0, NA)) +
facet_wrap(~cluster)
The shapes are broadly consistent, with the exception that the segment with early/high deaths is fairly low on total case counts. Suppose that county-level segments are instead created using a preponderance of cases rather than deaths:
# Create 3x2 clusters for hml and shape
popLoc <- "./RInputFiles/Coronavirus/covid_county_population_usafacts.csv"
cty_20201015_rule6_cases <- readRunUSAFacts(maxDate="2020-10-13",
popLoc=popLoc,
caseLoc=caseLoc,
deathLoc=deathLoc,
dlCaseDeath=!(file.exists(caseLoc) & file.exists(deathLoc)),
oldFile=readFromRDS("burden_20200903_new"),
existingStateClusters=test_hier6_201014$useClusters,
createClusters=TRUE,
hierarchical=NA,
minShape=4,
ratioDeathvsCase = 0.2,
ratioTotalvsShape = 0.25,
minDeath=100,
minCase=5000
)##
## -- Column specification --------------------------------------------------------
## cols(
## countyFIPS = col_double(),
## `County Name` = col_character(),
## State = col_character(),
## population = col_double()
## )##
## -- Column specification --------------------------------------------------------
## cols(
## .default = col_double(),
## `County Name` = col_character(),
## State = col_character()
## )
## i Use `spec()` for the full column specifications.## Rows: 849,870
## Columns: 6
## $ countyFIPS <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ countyName <chr> "Statewide Unallocated", "Statewide Unallocated", "State...
## $ state <chr> "AL", "AL", "AL", "AL", "AL", "AL", "AL", "AL", "AL", "A...
## $ stateFIPS <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,...
## $ date <date> 2020-01-22, 2020-01-23, 2020-01-24, 2020-01-25, 2020-01...
## $ cumCases <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...## Warning: `expand_scale()` is deprecated; use `expansion()` instead.##
## -- Column specification --------------------------------------------------------
## cols(
## .default = col_double(),
## `County Name` = col_character(),
## State = col_character()
## )
## i Use `spec()` for the full column specifications.## Rows: 849,870
## Columns: 6
## $ countyFIPS <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ countyName <chr> "Statewide Unallocated", "Statewide Unallocated", "State...
## $ state <chr> "AL", "AL", "AL", "AL", "AL", "AL", "AL", "AL", "AL", "A...
## $ stateFIPS <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,...
## $ date <date> 2020-01-22, 2020-01-23, 2020-01-24, 2020-01-25, 2020-01...
## $ cumDeaths <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...## Warning: `expand_scale()` is deprecated; use `expansion()` instead.## `summarise()` regrouping output by 'source' (override with `.groups` argument)
## `geom_smooth()` using formula 'y ~ x'## `summarise()` ungrouping output (override with `.groups` argument)
## `summarise()` ungrouping output (override with `.groups` argument)## # A tibble: 1 x 6
## cpm_mean_is0 dpm_mean_is0 cpm_mean_lt100 dpm_mean_lt100 cpm_mean_lt5000
## <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 0 0.0157 0 0.147 0.0559
## # ... with 1 more variable: dpm_mean_lt5000 <dbl>## `summarise()` regrouping output by 'state' (override with `.groups` argument)
## `summarise()` ungrouping output (override with `.groups` argument)
## `summarise()` ungrouping output (override with `.groups` argument)## `summarise()` regrouping output by 'cluster' (override with `.groups` argument)## `summarise()` ungrouping output (override with `.groups` argument)
## `summarise()` ungrouping output (override with `.groups` argument)## `summarise()` regrouping output by 'date', 'cluster' (override with `.groups` argument)## `summarise()` ungrouping output (override with `.groups` argument)
##
## Recency is defined as 2020-09-14 through current
##
## Recency is defined as 2020-09-14 through current## Warning: Removed 1 rows containing missing values (geom_point).## `summarise()` regrouping output by 'cluster' (override with `.groups` argument)## `summarise()` regrouping output by 'cluster' (override with `.groups` argument)
## `summarise()` regrouping output by 'cluster' (override with `.groups` argument)
## `summarise()` regrouping output by 'cluster' (override with `.groups` argument)
## Warning: `expand_scale()` is deprecated; use `expansion()` instead.
## Joining, by = "fipsCounty"## Joining, by = "fipsCounty"## `summarise()` regrouping output by 'cluster' (override with `.groups` argument)## `summarise()` ungrouping output (override with `.groups` argument)
Interestingly, there is no longer a segment with high deaths early since it is not well-differemtiated by having (relatively) high cases early. This is suggestive that deaths are a better metric than cases for tracking the historical impact of cornavirus by county (though given lags, cases are more likely to be a current leading indicator of burden).
Suppose that the movements by segment are examined:
# Function to examine the difference in case clusters and death clusters
examineCasevDeathClusters <- function(caseList,
deathList,
popMin=0,
ctyMin=0
) {
# FUNCTION ARGUMENTS:
# caseList: the processed object using cases
# deathList: the processed object using deaths
# popMin: in the text overlap grid, include a record if it has at least this much population
# ctyMin: in the text overlap grid, include a record if it has at least this many counties
# records will be included if they meet EITHER the popMin hurdle OR the ctyMin hurdle
# Pull the case cluster data
caseCluster <- caseList[["clusterStateData"]] %>%
count(fipsCounty, cluster) %>%
rename(caseCluster=cluster) %>%
select(-n)
# Integrate the death cluster data
case_death <- deathList[["clusterStateData"]] %>%
rename(deathCluster=cluster) %>%
full_join(caseCluster, by="fipsCounty")
# Plot the differences
p1 <- case_death %>%
group_by(fipsCounty, deathCluster, caseCluster) %>%
summarize(deaths=sum(deaths, na.rm=TRUE), cases=sum(cases, na.rm=TRUE), pop=mean(pop)) %>%
group_by(deathCluster, caseCluster) %>%
summarize(n=n(), pop=sum(pop), cases=sum(cases), deaths=sum(deaths)) %>%
filter(pop>=popMin | n >= ctyMin) %>%
ggplot(aes(y=deathCluster, x=caseCluster)) +
geom_tile(aes(fill=deaths/cases)) +
geom_text(aes(label=paste0("n=", n,
" (Pop: ", round(pop/1000000, 1),
")\nCases: ", round(cases/1000, 1),
"\nDeaths: ", round(deaths/1000, 1), " (", round(100*deaths/cases, 1), "%)"
)
),
size=2.5
) +
scale_fill_continuous("CFR", low="white", high="orange") +
labs(x="County cluster driven by cases",
y="County cluster driven by deaths",
title="County cluster comparison when driven by cases vs. deaths",
subtitle="n: # counties; Pop: population (millions); Cases: cases (000); Deaths: deaths (000) (CFR)"
)
if (ctyMin > 0 & popMin > 0) {
p1 <- p1 +
labs(caption=paste0("Includes overlaps with at least ",
ctyMin,
" counties OR at least ",
round(popMin/1000, 0),
" thousand population"
)
)
}
print(p1)
# Create a plotting data frame
plotData <- case_death %>%
group_by(date, deathCluster, caseCluster) %>%
summarize(deaths=sum(deaths, na.rm=TRUE), cases=sum(cases, na.rm=TRUE), pop=sum(pop), n=n()) %>%
ungroup() %>%
mutate(cpm=1000000*cases/pop, dpm=1000000*deaths/pop) %>%
group_by(deathCluster, caseCluster) %>%
mutate(cpm7=zoo::rollmean(cpm, k=7, fill=NA), dpm7=zoo::rollmean(dpm, k=7, fill=NA))
# Create plot for deaths per million trend by cluster overlap
p2 <- plotData %>%
ggplot(aes(x=date, y=dpm7)) +
geom_line(aes(group=deathCluster, color=deathCluster)) +
geom_text(data=~mutate(filter(., date==lubridate::round_date(max(date))), dpm7=Inf),
aes(label=paste0("n=", n), color=deathCluster),
hjust=1, vjust=1
) +
facet_grid(deathCluster ~ caseCluster, switch="y") +
labs(x="County case cluster",
y="County death cluster",
title="Deaths per million (7-day rolling mean) by county case cluster vs. county death cluster"
)
print(p2)
# Create plot for cases per million trend by cluster overlap
p3 <- plotData %>%
ggplot(aes(x=date, y=cpm7)) +
geom_line(aes(group=caseCluster, color=caseCluster)) +
geom_text(data=~mutate(filter(., date==lubridate::round_date(max(date))), cpm7=Inf),
aes(label=paste0("n=", n), color=caseCluster),
hjust=1, vjust=1
) +
facet_grid(deathCluster ~ caseCluster, switch="y") +
labs(x="County case cluster",
y="County death cluster",
title="Cases per million (7-day rolling mean) by county case cluster vs. county death cluster"
)
print(p3)
# Return a list of the two key frames
list(case_death=case_death, plotData=plotData)
}
# Run comparisons for cases and deaths
temp_caseDeath_001 <- examineCasevDeathClusters(caseList=cty_20201015_rule6_cases,
deathList=cty_20201015_rule6
)## `summarise()` regrouping output by 'fipsCounty', 'deathCluster' (override with `.groups` argument)## `summarise()` regrouping output by 'deathCluster' (override with `.groups` argument)## `summarise()` regrouping output by 'date', 'deathCluster' (override with `.groups` argument)
## Warning: Removed 42 row(s) containing missing values (geom_path).
## Warning: Removed 42 row(s) containing missing values (geom_path).
Significant variation in case fatality rate (deaths over confirmed cases) by both geography and time drive significant changes in the segments. Further, the “high cases” segment consists of multiple shapes, including a group that is early and a group that is late.
Next steps are to explore why the death clusters seem to be better differentiated on shape than the case clusters even when plotting using cases. Perhaps something is not working as intended when calculating distances based on shape of the curve.
Suppose that the key clustering data is pulled, using the adjustments above such that cases have 5x the weight as deaths:
shapeTestData <- cty_20201015_rule6_cases$clusterData$objCluster$dfCluster
shapeTestData %>%
summarize_if(is.numeric, .funs=list(sd)) %>%
mutate(all="all") %>%
pivot_longer(-all, names_to="metric", values_to="sd") %>%
ggplot(aes(x=fct_reorder(metric, sd), y=sd)) +
geom_col(fill="lightblue") +
geom_text(aes(y=sd+0.01, label=round(sd, 2)), hjust=0) +
coord_flip() +
labs(x="", y="Standard deviation", title="Standard deviation by scaled variable, clustering by cases")
The standard deviations appear appropriately weighted in favor of cases, though perhaps the 5:1 scaling is insufficient to overcome the significant disparities in death timing.
Suppose that a very pure segmentation by cases is attempted, and using data only from April-September:
# Create 3x2 clusters for hml and shape
popLoc <- "./RInputFiles/Coronavirus/covid_county_population_usafacts.csv"
cty_20201015_rule6_caseshvy <- readRunUSAFacts(maxDate="2020-10-13",
popLoc=popLoc,
caseLoc=caseLoc,
deathLoc=deathLoc,
dlCaseDeath=!(file.exists(caseLoc) & file.exists(deathLoc)),
oldFile=readFromRDS("burden_20200903_new"),
existingStateClusters=test_hier6_201014$useClusters,
createClusters=TRUE,
hierarchical=NA,
minShape=4,
maxShape=9,
ratioDeathvsCase = 0.001,
ratioTotalvsShape = 0.25,
minDeath=100,
minCase=5000
)##
## -- Column specification --------------------------------------------------------
## cols(
## countyFIPS = col_double(),
## `County Name` = col_character(),
## State = col_character(),
## population = col_double()
## )##
## -- Column specification --------------------------------------------------------
## cols(
## .default = col_double(),
## `County Name` = col_character(),
## State = col_character()
## )
## i Use `spec()` for the full column specifications.## Rows: 849,870
## Columns: 6
## $ countyFIPS <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ countyName <chr> "Statewide Unallocated", "Statewide Unallocated", "State...
## $ state <chr> "AL", "AL", "AL", "AL", "AL", "AL", "AL", "AL", "AL", "A...
## $ stateFIPS <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,...
## $ date <date> 2020-01-22, 2020-01-23, 2020-01-24, 2020-01-25, 2020-01...
## $ cumCases <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...## Warning: `expand_scale()` is deprecated; use `expansion()` instead.##
## -- Column specification --------------------------------------------------------
## cols(
## .default = col_double(),
## `County Name` = col_character(),
## State = col_character()
## )
## i Use `spec()` for the full column specifications.## Rows: 849,870
## Columns: 6
## $ countyFIPS <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ countyName <chr> "Statewide Unallocated", "Statewide Unallocated", "State...
## $ state <chr> "AL", "AL", "AL", "AL", "AL", "AL", "AL", "AL", "AL", "A...
## $ stateFIPS <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,...
## $ date <date> 2020-01-22, 2020-01-23, 2020-01-24, 2020-01-25, 2020-01...
## $ cumDeaths <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...## Warning: `expand_scale()` is deprecated; use `expansion()` instead.## `summarise()` regrouping output by 'source' (override with `.groups` argument)
## `geom_smooth()` using formula 'y ~ x'## `summarise()` ungrouping output (override with `.groups` argument)
## `summarise()` ungrouping output (override with `.groups` argument)## # A tibble: 1 x 6
## cpm_mean_is0 dpm_mean_is0 cpm_mean_lt100 dpm_mean_lt100 cpm_mean_lt5000
## <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 0 0.0157 0 0.147 0.0559
## # ... with 1 more variable: dpm_mean_lt5000 <dbl>## `summarise()` regrouping output by 'state' (override with `.groups` argument)
## `summarise()` ungrouping output (override with `.groups` argument)
## `summarise()` ungrouping output (override with `.groups` argument)## `summarise()` regrouping output by 'cluster' (override with `.groups` argument)## `summarise()` ungrouping output (override with `.groups` argument)
## `summarise()` ungrouping output (override with `.groups` argument)## `summarise()` regrouping output by 'date', 'cluster' (override with `.groups` argument)## `summarise()` ungrouping output (override with `.groups` argument)
##
## Recency is defined as 2020-09-14 through current
##
## Recency is defined as 2020-09-14 through current## Warning: Removed 1 rows containing missing values (geom_point).## `summarise()` regrouping output by 'cluster' (override with `.groups` argument)## `summarise()` regrouping output by 'cluster' (override with `.groups` argument)
## `summarise()` regrouping output by 'cluster' (override with `.groups` argument)
## `summarise()` regrouping output by 'cluster' (override with `.groups` argument)
## Warning: `expand_scale()` is deprecated; use `expansion()` instead.
## Joining, by = "fipsCounty"## Joining, by = "fipsCounty"## `summarise()` regrouping output by 'cluster' (override with `.groups` argument)## `summarise()` ungrouping output (override with `.groups` argument)
These segments appear much better tailored to differences in case volume and shape by county. A key finding is that even a modest weight on deaths (0.2 of cases) leads the segments to be overly driven by deaths. This is likely driven by the difference in deaths, especially among early/high, being extremely differentiated from each other over s short period of time (which will produce a very high euclidean distance that needs to be substantially scaled down to not dominate)
The comparisons of death and cases can again be run, with the main text overlap grid using only cases with at least 5 counties OR at least 500,000 population:
# Run comparisons for cases and deaths
temp_caseDeath_002 <- examineCasevDeathClusters(caseList=cty_20201015_rule6_caseshvy,
deathList=cty_20201015_rule6,
ctyMin=5,
popMin=500000
)## `summarise()` regrouping output by 'fipsCounty', 'deathCluster' (override with `.groups` argument)## `summarise()` regrouping output by 'deathCluster' (override with `.groups` argument)## `summarise()` regrouping output by 'date', 'deathCluster' (override with `.groups` argument)
## Warning: Removed 42 row(s) containing missing values (geom_path).
## Warning: Removed 42 row(s) containing missing values (geom_path).
The switching among segments is now largely driven by differences in CFR, as desired. The plots of deaths by cluster overlap appear driven by death cluster while the plots of cases by cluster overlap appear driven by case cluster. These segments appear suitable for further analysis.
The rules-based segmentation approach is updated to allow for more user control (defining the number of segments by dimension and setting a seed):
# Updated preparation file for county clustering
prepClusterCounties <- function(burdenFile,
maxDate,
minPop,
createClusters=TRUE,
hierarchical=FALSE,
returnList=!hierarchical,
...
) {
# FUNCTION ARGUMENTS:
# burdenFile: the pivoted file containing the burdens data
# maxDate: the latest date to be used from the data
# minPop: the smallest population for a county to be included
# createClusters: boolean, whether to create county-level clusters (if FALSE, object returned as NULL)
# FALSE would be for prepping and converting data only (those objects are returned)
# hierarchical: whether to create hierarchical clusters
# TRUE means run hierarchical clustering
# FALSE means run kmeans clustering
# NA means run rules-based clustering
# returnList: whether the clustering call returns a list (only set up for TRUE as of now)
# ...: other arguments to be passed to clusterStates
# STEP 1: Select only desired variables from burden file
countyCumPerCapita <- burdenFile %>%
select(state=countyFIPS, date, cpm=cumCasesPer, dpm=cumDeathPer, population) %>%
arrange(state, date)
# STEP 2: Confirm that there are no duplicates and that every county has the same dates
# This should be 1 provided that there are no duplicates
countyCumPerCapita %>%
count(state, date) %>%
pull(n) %>%
max()
# This should have no standard deviation if the same number of records exist on every day
countyCumPerCapita %>%
mutate(n=1) %>%
group_by(date) %>%
summarize(n=sum(n), population=sum(population)) %>%
summarize_at(vars(all_of(c("n", "population"))), .funs=list(sd=sd, min=min, max=max))
# STEP 3: Convert to daily new totals rather than cumulative data
countyDailyPerCapita <- countyCumPerCapita %>%
group_by(state) %>%
arrange(date) %>%
mutate_at(vars(all_of(c("cpm", "dpm"))), ~ifelse(row_number()==1, ., .-lag(.))) %>%
ungroup()
# STEP 4: Add rolling 7 aggregates and total cases/deaths
countyDailyPerCapita <- countyDailyPerCapita %>%
arrange(state, date) %>%
group_by(state) %>%
helperRollingAgg(origVar="cpm", newName="cpm7", k=7) %>%
helperRollingAgg(origVar="dpm", newName="dpm7", k=7) %>%
ungroup() %>%
mutate(cases=cpm*population/1000000, deaths=dpm*population/1000000)
# STEP 5: Filter the data prior to clustering
countyFiltered <- countyDailyPerCapita %>%
filter(population >= minPop, date <= as.Date(maxDate)) %>%
mutate(state=as.character(state))
# STEP 6: Check the implications of the filtering
# Step 6a: Check whether minDeath and minCase have been passed
ellipsisList <- (list(...))
if ("minCase" %in% names(ellipsisList)) {
minCase <- ellipsisList[["minCase"]]
cat("\nShape curves will impose a floor of at least", minCase, "cases per million")
} else {
cat("\nShapes will be created without any floor on the number of cases per million")
minCase <- 0
}
if ("minDeath" %in% names(ellipsisList)) {
minDeath <- ellipsisList[["minDeath"]]
cat("\nShape curves will impose a floor of at least", minDeath, "deaths per million")
} else {
cat("\nShapes will be created without any floor on the number of deaths per million")
minDeath <- 0
}
# Check number of counties that fail the test for minDeath deaths per million or minCase cases per million
is0 <- function(x) mean(x==0)
isltn <- function(x, n) mean(x<n)
isltDeath <- function(x) isltn(x, n=minDeath)
isltCase <- function(x) isltn(x, n=minCase)
cat("\n*** Counties with 0 cases/deaths or that fall below the floor for minCase/minDeath ***\n")
countyFiltered %>%
group_by(state) %>%
summarize_at(c("cpm", "dpm"), sum) %>%
ungroup() %>%
summarize_at(vars(all_of(c("cpm", "dpm"))),
.funs=list(mean_is0=is0, mean_ltDeath=isltDeath, mean_ltCase=isltCase)
) %>%
select(-cpm_mean_ltDeath, -dpm_mean_ltCase) %>%
print()
# Run county-level clusters if requested, otherwise store as NULL
objCluster <- if(createClusters) {
clusterStates(countyFiltered, hierarchical=hierarchical, returnList=returnList, ...)
} else {
NULL
}
# Return all of the relevant objects
list(objCluster=objCluster,
countyFiltered=countyFiltered,
countyDailyPerCapita=countyDailyPerCapita,
countyCumPerCapita=countyCumPerCapita
)
}
# Updateds to the clustering function
clusterStates <- function(df,
caseVar="cpm",
deathVar="dpm",
shapeFunc=lubridate::month,
minShape=NULL,
maxShape=NULL,
minDeath=0,
maxDeath=Inf,
minCase=0,
maxCase=Inf,
ratioTotalvsShape=1,
ratioDeathvsCase=1,
hierarchical=TRUE,
hierMethod="complete",
nCenters=3,
iter.max=10,
nstart=1,
testCenters=NULL,
returnList=FALSE,
hmlSegs=3,
eslSegs=2,
seed=NULL
) {
# FUNCTION ARGUMENTS:
# df: the data frame containing cases and deaths data
# caseVar: the variable containing the cases per capita data
# deathVar: the variable containing the deaths per capita data
# shapeFunc: the function to be used for creating the shape of the curve
# minShape: the minimum value to be used for shape (to avoid very small amounts of data in Jan/Feb/Mar)
# shape is the month, so 4 means start with April data (NULL means keep everything)
# maxShape: the maximum value to be used for shape (to avoid very small amounts of data in a partial month)
# shape is the month, so 9 means end with September data (NULL means keep everything)
# minDeath: use this value as a floor for the death metric when calculating shape
# maxDeath: use this value as a maximum when calculating distance using deaths
# minCase: use this value as a floor for the case metric when calculating shape
# maxCase: use this value as a maximum when calculating distance using cases
# ratioTotalvsShape: amount of standard deviation to be kept in total variable vs shape variables
# ratioDeathvsCase: amount of standard deviation to be kept in deaths vs cases
# (total death data will be scaled to have sd this many times higher than cases)
# (death percentages by time period will be scaled directly by this amount)
# hierarchical: whether to create hierarchical clusters
# TRUE means run hierarchical clustering
# FALSE means run kmeans clustering
# NA means run rules-based clustering
# hierMethod: the method for hierarchical clustering (e.g., 'complete' or 'single')
# nCenters: the number of centers to use for kmeans clustering
# testCenters: integer vector of centers to test (will create an elbow plot); NULL means do not test
# iter.max: maximumum number of kmeans iterations (default in kmeans algorithm is 10)
# nstart: number of random sets chosen for kmeans (default in kmeans algorithm is 1)
# returnList: boolean, if FALSE just the cluster object is returned
# if TRUE, a list is returned with dfCluster and the cluster object
# hmlSegs: number of segments to create for volume of burden integrated over time
# eslSegs: number of segments to create for shape of burden over time
# seed: set the seed to this value (NULL means no seed)
# Extract key information (aggregates and by shapeFunc for each state)
df <- df %>%
select_at(vars(all_of(c("date", "state", caseVar, deathVar)))) %>%
purrr::set_names(c("date", "state", "cases", "deaths")) %>%
mutate(timeBucket=shapeFunc(date)) %>%
group_by(state, timeBucket) %>%
summarize(cases=sum(cases), deaths=sum(deaths)) %>%
ungroup()
# Limit to only relevant time buckets if requested
if (!is.null(minShape)) {
df <- df %>%
filter(timeBucket >= minShape)
}
if (!is.null(maxShape)) {
df <- df %>%
filter(timeBucket <= maxShape)
}
# Extract an aggregate by state, scaled so that they have the proper ratio
dfAgg <- df %>%
group_by(state) %>%
summarize(totalCases=sum(cases), totalDeaths=sum(deaths)) %>%
mutate(totalCases=pmin(totalCases, maxCase), totalDeaths=pmin(totalDeaths, maxDeath)) %>%
ungroup() %>%
mutate(totalDeaths=ratioDeathvsCase*totalDeaths*sd(totalCases)/sd(totalDeaths))
# Extract the percentages (shapes) by month, scaled so that they have the proper ratio
dfShape <- df %>%
pivot_longer(-c(state, timeBucket)) %>%
group_by(state, name) %>%
mutate(tot=pmax(sum(value), ifelse(name=="deaths", minDeath, minCase)),
value=ifelse(name=="deaths", ratioDeathvsCase, 1) * value / tot) %>%
select(-tot) %>%
pivot_wider(state, names_from=c(name, timeBucket), values_from=value) %>%
ungroup()
# Function to calculate SD of a subset of columns
calcSumSD <- function(df) {
df %>%
ungroup() %>%
select(-state) %>%
summarize_all(.funs=sd) %>%
as.vector() %>%
sum()
}
# Down-weight the aggregate data so that there is the proper sum of sd in aggregates and shapes
aggSD <- calcSumSD(dfAgg)
shapeSD <- calcSumSD(dfShape)
dfAgg <- dfAgg %>%
mutate_if(is.numeric, ~. * ratioTotalvsShape * shapeSD / aggSD)
# Combine so there is one row per state
dfCluster <- dfAgg %>%
inner_join(dfShape, by="state")
# convert 'state' to rowname
keyData <- dfCluster %>% column_to_rownames("state")
# Create rules-based segments (NA) or hierarchical segments (TRUE) or kmeans segments (FALSE)
if (is.na(hierarchical)) {
# Create pseudo-rules-based segments
if (!is.null(seed)) set.seed(seed)
# STEP 1: Classify high-medium-low based on deaths and cases
hml <- kmeans(select(keyData, starts_with("total")),
centers=hmlSegs, iter.max=iter.max, nstart=nstart
)
# STEP 2: Classify early-late based on shape
esl <- kmeans(select(keyData, -starts_with("total")),
centers=eslSegs, iter.max=iter.max, nstart=nstart
)
# STEP 3: Create a final segment
objCluster <- eslSegs*(hml$cluster-1) + esl$cluster
} else if (isTRUE(hierarchical)) {
# Create hierarchical segments
objCluster <- hclust(dist(keyData), method=hierMethod)
plot(objCluster)
} else {
# Create k-means segments
# Create an elbow plot if testCenters is not NULL
if (!is.null(testCenters)) {
helperElbow(keyData, testCenters=testCenters, iter.max=iter.max, nstart=nstart, silhouette=TRUE)
}
# Create the kmeans cluster object, setting a seed if requested
if (!is.null(seed)) set.seed(seed)
objCluster <- kmeans(keyData, centers=nCenters, iter.max=iter.max, nstart=nstart)
cat("\nCluster means and counts\n")
n=objCluster$size %>% cbind(objCluster$centers) %>% round(2) %>% t() %>% print()
}
# Return the data and object is a list if returnList is TRUE, otherwise return only the clustering object
if (!isTRUE(returnList)) {
objCluster
} else {
list(objCluster=objCluster, dfCluster=dfCluster)
}
}Further, the assessClusters() and helperMakeStateClusterData() functions are updated so they can order based on EITHER cpm or dpm based on function arguments:
# Updated function for cluster assessment
# Main function creates the required data and calls helper functions
# 1. Call helper to create overall cluster summary
# 2. Call helper function for totals and per capita by state
# 3. Call helper for recent vs. overall cases
# 4. Call helper for evolution of segment aggregate and individual components
assessClusters <- function(clusters,
dfState=stateData,
dfBurden=cvFilteredPerCapita,
thruLabel="Aug 20, 2020",
isCounty=FALSE,
plotsTogether=FALSE,
clusterPlotsTogether=plotsTogether,
recentTotalTogether=plotsTogether,
clusterAggTogether=plotsTogether,
makeSummaryPlots=TRUE,
makeTotalvsPerCapitaPlots=!isCounty,
makeRecentvsTotalPlots=TRUE,
makeTotalvsElementPlots=TRUE,
showMap=!isCounty,
orderCluster=FALSE
) {
# FUNCTION ARGUMENTS:
# clusters: the named vector containing the clusters by state
# dfState: the file containing the states and populations
# dfBurden: the data containing the relevant per capita burden statistics by state-date
# thruLabel: label for plots for 'data through'
# isCounty: boolean, is this a plot of county-level data that have been named 'state'?
# FALSE means that it is normal state-level data
# plotsTogether: boolean, should plots be consolidated on fewer pages?
# clusterPlotsTogether: boolean, should plots p1-p4 be consolidated?
# recentTotalTogether: boolean, should recent total plots p7-p8 be consolidated?
# clusterAggTogether: boolean, should aggregate plots p9/p11 and p10/p12 be consolidated?
# makeSummaryPlots: boolean, should the summary plots be made?
# makeTotalvsPerCapitaPlots: boolean, should the total and per capita plots be produced?
# makeRecentvsTotalPlots: boolean, should the recent vs. total plots be created?
# makeTotalvsElementPlots: boolean, should the total vs. element plots be created?
# showMap: boolean, whether to create a map colored by cluster (will show segment counts otherwise)
# orderCluster: if FALSE, ignore; if TRUE, order by "dpm"; if anything else, order by orderCluster
# ...: any additional arguments passed from a calling function
# most common would be orderCluster=TRUE, a request for the clusters to be ordered by disease burden
# Attach the clusters to the state population data
dfState <- as.data.frame(clusters) %>%
set_names("cluster") %>%
rownames_to_column("state") %>%
inner_join(dfState, by="state") %>%
mutate(cluster=factor(cluster))
# Plot the rolling 7-day mean dialy disease burden by cluster
dfPlot <- dfState %>%
inner_join(dfBurden, by="state") %>%
tibble::as_tibble()
# Reorder the clusters if requested
if (!isFALSE(orderCluster)) {
if (isTRUE(orderCluster)) burdenParam <- "dpm" else burdenParam <- orderCluster
dfPlot <- changeOrderLabel(dfPlot, grpVars="state", burdenVar=burdenParam)
}
# Call the helper function to make the overall summary statistics
if (makeSummaryPlots) {
helperClusterSummaryPlots(dfState=dfState,
dfPlot=dfPlot,
showMap=showMap,
clusterPlotsTogether=clusterPlotsTogether,
mapLevel=if(isCounty) "counties" else "states"
)
}
# These are primarily useful for state-level data and default to FALSE when isCounty is TRUE
if (makeTotalvsPerCapitaPlots) {
helperCallTotalPerCapita(dfPlot=dfPlot, thruLabel=thruLabel)
}
# Call the helper function to make recent vs. total plots
if (makeRecentvsTotalPlots) {
helperCallRecentvsTotal(dfPlot=dfPlot,
thruLabel=thruLabel,
labelPoints=!isCounty,
recentTotalTogether = recentTotalTogether
)
}
# Call the total vs. elements helper function
if (makeTotalvsElementPlots) {
helperCallTotalvsElements(dfPlot=dfPlot,
aggAndTotal=!isCounty,
clusterAggTogether=clusterPlotsTogether
)
}
# Return the plotting data frame
dfPlot
}
# Create the cluster-state data
helperMakeClusterStateData <- function(dfPlot,
dfPop=usmap::countypop,
dfBurden=countyDailyPerCapita,
orderCluster=FALSE
) {
# FUNCTION ARGUMENTS:
# dfPlot: the raw plotting data (which can have factors already reordered)
# dfPop: source for full county data with population
# dfBurden: source for disease burden by geography
# orderCluster: if FALSE, ignore; if TRUE, order by "dpm"; if anything else, order by orderCluster
# Merge in the counties that were previously excluded from segmentation
df <- dfPlot %>%
filter(date==max(date)) %>%
select(fipsCounty=state, cluster) %>%
mutate(fipsCounty=stringr::str_pad(fipsCounty, width=5, side="left", pad="0")) %>%
right_join(select(dfPop, fipsCounty=fips, state=abbr, countyName=county, pop=pop_2015)) %>%
mutate(cluster=factor(ifelse(is.na(cluster), 999, as.character(cluster))))
# Merge in the disease data
df <- dfBurden %>%
mutate(fipsCounty=stringr::str_pad(state, width=5, side="left", pad="0")) %>%
select(-state, -population) %>%
right_join(df)
# Reorder the clusters if requested
if (!isFALSE(orderCluster)) {
if (isTRUE(orderCluster)) burdenParam <- "dpm" else burdenParam <- orderCluster
df <- changeOrderLabel(df, grpVars="fipsCounty", burdenVar=burdenParam)
}
# Return the relevant frame
df
}The updated segments can be run using 3x3, and with a seed set:
# Create 3x3 clusters for hml and shape
popLoc <- "./RInputFiles/Coronavirus/covid_county_population_usafacts.csv"
cty_20201015_rule9_caseshvy <- readRunUSAFacts(maxDate="2020-10-13",
popLoc=popLoc,
caseLoc=caseLoc,
deathLoc=deathLoc,
dlCaseDeath=!(file.exists(caseLoc) & file.exists(deathLoc)),
oldFile=readFromRDS("burden_20200903_new"),
existingStateClusters=test_hier6_201014$useClusters,
createClusters=TRUE,
hierarchical=NA,
minShape=4,
maxShape=9,
ratioDeathvsCase = 0.001,
ratioTotalvsShape = 0.25,
minDeath=100,
minCase=5000,
hmlSegs=3,
eslSegs=3,
seed=2010221422,
orderCluster="cpm"
)##
## -- Column specification --------------------------------------------------------
## cols(
## countyFIPS = col_double(),
## `County Name` = col_character(),
## State = col_character(),
## population = col_double()
## )##
## -- Column specification --------------------------------------------------------
## cols(
## .default = col_double(),
## `County Name` = col_character(),
## State = col_character()
## )
## i Use `spec()` for the full column specifications.## Rows: 849,870
## Columns: 6
## $ countyFIPS <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ countyName <chr> "Statewide Unallocated", "Statewide Unallocated", "State...
## $ state <chr> "AL", "AL", "AL", "AL", "AL", "AL", "AL", "AL", "AL", "A...
## $ stateFIPS <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,...
## $ date <date> 2020-01-22, 2020-01-23, 2020-01-24, 2020-01-25, 2020-01...
## $ cumCases <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...## Warning: `expand_scale()` is deprecated; use `expansion()` instead.##
## -- Column specification --------------------------------------------------------
## cols(
## .default = col_double(),
## `County Name` = col_character(),
## State = col_character()
## )
## i Use `spec()` for the full column specifications.## Rows: 849,870
## Columns: 6
## $ countyFIPS <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ countyName <chr> "Statewide Unallocated", "Statewide Unallocated", "State...
## $ state <chr> "AL", "AL", "AL", "AL", "AL", "AL", "AL", "AL", "AL", "A...
## $ stateFIPS <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,...
## $ date <date> 2020-01-22, 2020-01-23, 2020-01-24, 2020-01-25, 2020-01...
## $ cumDeaths <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...## Warning: `expand_scale()` is deprecated; use `expansion()` instead.## `summarise()` regrouping output by 'source' (override with `.groups` argument)
## `geom_smooth()` using formula 'y ~ x'## `summarise()` ungrouping output (override with `.groups` argument)
## `summarise()` ungrouping output (override with `.groups` argument)##
## Shape curves will impose a floor of at least 5000 cases per million
## Shape curves will impose a floor of at least 100 deaths per million
## *** Counties with 0 cases/deaths or that fall below the floor for minCase/minDeath ***
## # A tibble: 1 x 4
## cpm_mean_is0 dpm_mean_is0 dpm_mean_ltDeath cpm_mean_ltCase
## <dbl> <dbl> <dbl> <dbl>
## 1 0 0.0157 0.147 0.0559## `summarise()` regrouping output by 'state' (override with `.groups` argument)
## `summarise()` ungrouping output (override with `.groups` argument)
## `summarise()` ungrouping output (override with `.groups` argument)## `summarise()` regrouping output by 'cluster' (override with `.groups` argument)## `summarise()` ungrouping output (override with `.groups` argument)
## `summarise()` ungrouping output (override with `.groups` argument)## `summarise()` regrouping output by 'date', 'cluster' (override with `.groups` argument)## `summarise()` ungrouping output (override with `.groups` argument)
##
## Recency is defined as 2020-09-14 through current
##
## Recency is defined as 2020-09-14 through current## Warning: Removed 1 rows containing missing values (geom_point).## `summarise()` regrouping output by 'cluster' (override with `.groups` argument)## `summarise()` regrouping output by 'cluster' (override with `.groups` argument)
## `summarise()` regrouping output by 'cluster' (override with `.groups` argument)
## `summarise()` regrouping output by 'cluster' (override with `.groups` argument)
## Warning: `expand_scale()` is deprecated; use `expansion()` instead.
## Joining, by = "fipsCounty"## Joining, by = "fipsCounty"## `summarise()` regrouping output by 'cluster' (override with `.groups` argument)## `summarise()` ungrouping output (override with `.groups` argument)
The shapes of the segments are intriguing, with generally:
- Early (Apr-Jun) disease in a classic pandemic curve
- Middle (Jul-Sep) disease in a classic pandemic curve
- Late disease that does not yet show the downslope of the pandemic curve
Additionally, the “lower burden” version of each of the shapes shows more signs of rebounding than the “higher burdens” of the same shape. Further exploration is a next step.
The overlap of death clusters and case cluster can again be explored:
# Run comparisons for cases and deaths
temp_caseDeath_003 <- examineCasevDeathClusters(caseList=cty_20201015_rule9_caseshvy,
deathList=cty_20201015_rule6,
ctyMin=5,
popMin=500000
)## `summarise()` regrouping output by 'fipsCounty', 'deathCluster' (override with `.groups` argument)## `summarise()` regrouping output by 'deathCluster' (override with `.groups` argument)## `summarise()` regrouping output by 'date', 'deathCluster' (override with `.groups` argument)
## Warning: Removed 42 row(s) containing missing values (geom_path).
## Warning: Removed 60 row(s) containing missing values (geom_path).
Overlaps are as expected, with good diferentiation of death volumes and shapes by death cluster and good differentiation of case volumes and shapes by case cluster. And, the overlaps between the death cluster and case cluster are closely tied to the case fatality rate (CFR), at least in overlap buckets where there is “sufficient” data volume.
Suppose that the objective is to look for counties that have had significant increases in case volumes after September (the final data used for the shape portion of the segmentation):
octCases <- cty_20201015_rule9_caseshvy$clusterStateData %>%
filter(!is.na(date)) %>%
mutate(dateType = factor(ifelse(date >= "2020-10-01", "Oct", "Jan-Sep"), levels=c("Jan-Sep", "Oct"))) %>%
group_by(fipsCounty, cluster, dateType) %>%
summarize(cpm=sum(cpm), dpm=sum(dpm), pop=mean(pop))## `summarise()` regrouping output by 'fipsCounty', 'cluster' (override with `.groups` argument)octCases %>%
pivot_wider(c(fipsCounty, cluster, pop), names_from="dateType", values_from=cpm) %>%
ggplot(aes(x=`Jan-Sep`/1000, y=Oct/1000)) +
geom_point(aes(color=cluster, size=pop), alpha=0.25) +
geom_smooth(aes(group=cluster, color=cluster), se=FALSE, method="lm") +
labs(x="Cases per million (Jan-Sep), in thousands",
y="Cases per million (Oct), in thousands",
title="Cases per million by cluster and time period (000s)"
) +
facet_wrap(~cluster, scales="free") +
xlim(c(0, NA)) +
ylim(c(0, NA))## `geom_smooth()` using formula 'y ~ x'
octCases %>%
pivot_wider(c(fipsCounty, cluster, pop), names_from="dateType", values_from=cpm) %>%
filter(cluster != 999) %>%
ggplot(aes(x=`Jan-Sep`/1000, y=Oct/1000)) +
geom_point(aes(color=cluster, size=pop), alpha=0.25) +
geom_smooth(aes(group=cluster, color=cluster), se=FALSE, method="lm") +
labs(x="Cases per million (Jan-Sep), in thousands",
y="Cases per million (Oct), in thousands",
title="Cases per million by cluster and time period (000s)",
subtitle="Excludes counties < 25k population"
) +
facet_wrap(~cluster)## `geom_smooth()` using formula 'y ~ x'
octCases %>%
pivot_wider(c(fipsCounty, cluster, pop), names_from="dateType", values_from=cpm) %>%
filter(cluster != 999) %>%
ggplot(aes(x=`Jan-Sep`/1000, y=Oct/1000)) +
geom_point(aes(color=cluster, size=pop), alpha=0.25) +
geom_smooth(aes(group=cluster, color=cluster, weight=pop), se=FALSE, method="lm") +
geom_smooth(aes(weight=pop), se=FALSE, method="lm", color="black", lty=2) +
labs(x="Cases per million (Jan-Sep), in thousands",
y="Cases per million (Oct), in thousands",
title="Cases per million by cluster and time period (000s)",
subtitle="Excludes counties < 25k population",
caption="Smooths driven by lm, with points weighted by population"
)## `geom_smooth()` using formula 'y ~ x'## `geom_smooth()` using formula 'y ~ x'
In general, higher impacted elements in Jan-Sep within a given cluster tend to be higher impacted in October also. Suppose that counties with at least 10,000 cases per million through mid-October are of interest:
octCases %>%
filter(dateType=="Oct", cpm >= 10000) %>%
group_by(cluster) %>%
summarize(n=n(), cpm=sum(cpm*pop)/sum(pop), dpm=sum(dpm*pop)/sum(pop), mean_pop=mean(pop))## `summarise()` ungrouping output (override with `.groups` argument)## # A tibble: 3 x 5
## cluster n cpm dpm mean_pop
## <fct> <int> <dbl> <dbl> <dbl>
## 1 4 4 11199. 248. 47598
## 2 7 8 13412. 79.7 75850.
## 3 999 88 13878. 149. 7543.By and large, counties with large “spikes” in early October tend to be the small counties where it is easy to get a spike with just a few cases. There are a handful of larger counties that also meet this threshold.
One common threshold used is 150 cases per million. Since the October data extends through October 13, this would be roughly 2000 cases per million. Suppose that a “spike” is defined as a pace that is at least double this threshold:
octCases %>%
filter(dateType=="Oct") %>%
group_by(cluster) %>%
summarize(nTotal=n(), n_gt4k=sum(cpm>=4000), pct_gt4k=n_gt4k/nTotal,
cpm_gt4k=sum(cpm*pop*(cpm>=4000))/sum(pop*(cpm>=4000)),
dpm_gt4k=sum(dpm*pop*(cpm>=4000))/sum(pop*(cpm>=4000)),
mean_pop_total=mean(pop), mean_pop_gt4k=sum(pop*(cpm>=4000))/sum(cpm>=4000)
) %>%
arrange(-pct_gt4k)## `summarise()` ungrouping output (override with `.groups` argument)## # A tibble: 10 x 8
## cluster nTotal n_gt4k pct_gt4k cpm_gt4k dpm_gt4k mean_pop_total mean_pop_gt4k
## <fct> <int> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 4 39 27 0.692 7475. 101. 74883. 64147.
## 2 7 217 98 0.452 6428. 52.6 87802. 79294.
## 3 999 1536 451 0.294 7012. 92.1 11548. 10503.
## 4 5 36 10 0.278 5881. 53.2 157476. 94447.
## 5 1 254 46 0.181 5379. 30.7 73653. 60350.
## 6 6 151 19 0.126 4713. 42.1 159557. 101983.
## 7 9 324 19 0.0586 5097. 47.0 300867. 177906.
## 8 8 113 6 0.0531 5369. 16.7 491847. 110445.
## 9 3 248 5 0.0202 4980. 9.21 195366. 65077.
## 10 2 209 2 0.00957 4445. 12.6 143269. 121118.There is significant variation in the likelihood of a county experiencing ~300 cases per million per day during early October:
- A majority of the counties experiencing heavy and late disease
- Roughly half of the counties experiencing moderate and late case burden
- Roughly a quarter of small counties (<25k population) and counties that experienced early and heavy burden
- Roughly 15% of counties experience light and late case burden and counties that experienced heavy and mid-period case burden
- Few counties that experienced mild-moderate burden either early or mod-term
In general, population for counties experiencing high disease burden in early October is less than average population for counties in the cluster.
These counties can be plotted on the US map:
octCases %>%
filter(dateType=="Oct", cpm >= 4000) %>%
select(fips=fipsCounty, cluster) %>%
usmap::plot_usmap(regions="counties", data=., values="cluster") +
scale_fill_discrete("Cluster", na.value="lightgrey") +
labs(title="Counties averaging 300+ cases per million per day (October 1-13)")
There is a strong geographic clustering of the high-case counties which is suggestive of prevalence increase rather than spurious spikes based solely on small denominators. Next steps are to refresh that data and see how burden by segment has evolved through mid-late October.
The three main data downloads are run again:
# Use existing segments with updated data
locDownload <- "./RInputFiles/Coronavirus/CV_downloaded_201024.csv"
test_old_201023 <- readRunCOVIDTrackingProject(thruLabel="Oct 23, 2020",
downloadTo=if (file.exists(locDownload)) NULL else locDownload,
readFrom=locDownload,
compareFile=test_old_201014$dfRaw,
useClusters=readFromRDS("test_hier5_201001")$useClusters
)##
## -- Column specification --------------------------------------------------------
## cols(
## .default = col_double(),
## state = col_character(),
## totalTestResultsSource = col_character(),
## dataQualityGrade = col_character(),
## lastUpdateEt = col_character(),
## dateModified = col_datetime(format = ""),
## checkTimeEt = col_character(),
## dateChecked = col_datetime(format = ""),
## fips = col_character(),
## hash = col_character(),
## grade = col_logical()
## )
## i Use `spec()` for the full column specifications.##
## File is unique by state and date
##
##
## Overall control totals in file:
## # A tibble: 1 x 3
## positiveIncrease deathIncrease hospitalizedCurrently
## <dbl> <dbl> <dbl>
## 1 8449120 215761 8644560
##
## *** COMPARISONS TO REFERENCE FILE: compareFile
##
## Checkin for similarity of: column names
## In reference but not in current:
## In current but not in reference:
##
## Checkin for similarity of: states
## In reference but not in current:
## In current but not in reference:
##
## Checkin for similarity of: dates
## In reference but not in current:
## In current but not in reference: 2020-10-23 2020-10-22 2020-10-21 2020-10-20 2020-10-19 2020-10-18 2020-10-17 2020-10-16 2020-10-15
##
## *** Difference of at least 5 and difference is at least 1%:## Joining, by = c("date", "name")## date name newValue oldValue
## 1 2020-03-28 positiveIncrease 19925 19692
## 2 2020-03-28 deathIncrease 544 538
## 3 2020-03-29 positiveIncrease 19348 19581
## 4 2020-03-29 deathIncrease 515 521
## 5 2020-10-05 hospitalizedCurrently 31299 30098
## 6 2020-10-06 hospitalizedCurrently 32595 31346
## 7 2020-10-07 hospitalizedCurrently 33457 32105## Joining, by = c("date", "name")## Warning: Removed 9 row(s) containing missing values (geom_path).##
##
## *** Difference of at least 5 and difference is at least 1%:## Joining, by = c("state", "name")## state name newValue oldValue
## 1 MO hospitalizedCurrently 164189 160171
## Rows: 13,101
## Columns: 55
## $ date <date> 2020-10-23, 2020-10-23, 2020-10-23, 20...
## $ state <chr> "AK", "AL", "AR", "AS", "AZ", "CA", "CO...
## $ positive <dbl> 13161, 180916, 104135, 0, 235882, 88686...
## $ probableCases <dbl> NA, 25001, 6796, NA, 5394, NA, 6401, 26...
## $ negative <dbl> 539585, 1133858, 1170162, 1616, 1450981...
## $ pending <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,...
## $ totalTestResultsSource <chr> "totalTestsViral", "totalTestsViral", "...
## $ totalTestResults <dbl> 552746, 1289773, 1267501, 1616, 1681469...
## $ hospitalizedCurrently <dbl> 59, 888, 610, NA, 815, 3011, 547, 233, ...
## $ hospitalizedCumulative <dbl> NA, 19595, 6678, NA, 20967, NA, 8478, 1...
## $ inIcuCurrently <dbl> NA, NA, 237, NA, 172, 754, NA, NA, 23, ...
## $ inIcuCumulative <dbl> NA, 2001, NA, NA, NA, NA, NA, NA, NA, N...
## $ onVentilatorCurrently <dbl> 7, NA, 92, NA, 85, NA, NA, NA, 8, NA, N...
## $ onVentilatorCumulative <dbl> NA, 1148, 807, NA, NA, NA, NA, NA, NA, ...
## $ recovered <dbl> 6915, 74439, 93215, NA, 39306, NA, 7394...
## $ dataQualityGrade <chr> "A", "A", "A+", "D", "A+", "B", "A", "B...
## $ lastUpdateEt <chr> "10/23/2020 03:59", "10/23/2020 11:00",...
## $ dateModified <dttm> 2020-10-23 03:59:00, 2020-10-23 11:00:...
## $ checkTimeEt <chr> "10/22 23:59", "10/23 07:00", "10/22 20...
## $ death <dbl> 68, 2859, 1782, 0, 5865, 17262, 2070, 4...
## $ hospitalized <dbl> NA, 19595, 6678, NA, 20967, NA, 8478, 1...
## $ dateChecked <dttm> 2020-10-23 03:59:00, 2020-10-23 11:00:...
## $ totalTestsViral <dbl> 552746, 1289773, 1267501, 1616, NA, 174...
## $ positiveTestsViral <dbl> 11644, NA, NA, NA, NA, NA, NA, NA, NA, ...
## $ negativeTestsViral <dbl> 540786, NA, 1170162, NA, NA, NA, NA, NA...
## $ positiveCasesViral <dbl> 13161, 155915, 97339, 0, 230488, 886865...
## $ deathConfirmed <dbl> 68, 2674, 1626, NA, 5578, NA, NA, 3674,...
## $ deathProbable <dbl> NA, 185, 156, NA, 287, NA, NA, 903, NA,...
## $ totalTestEncountersViral <dbl> NA, NA, NA, NA, NA, NA, 1764648, NA, 48...
## $ totalTestsPeopleViral <dbl> NA, NA, NA, NA, 1681469, NA, 1113799, N...
## $ totalTestsAntibody <dbl> NA, NA, NA, NA, 311920, NA, 178626, NA,...
## $ positiveTestsAntibody <dbl> NA, NA, NA, NA, NA, NA, 12684, NA, NA, ...
## $ negativeTestsAntibody <dbl> NA, NA, NA, NA, NA, NA, 165942, NA, NA,...
## $ totalTestsPeopleAntibody <dbl> NA, 62498, NA, NA, NA, NA, NA, NA, NA, ...
## $ positiveTestsPeopleAntibody <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,...
## $ negativeTestsPeopleAntibody <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,...
## $ totalTestsPeopleAntigen <dbl> NA, NA, 44666, NA, NA, NA, NA, NA, NA, ...
## $ positiveTestsPeopleAntigen <dbl> NA, NA, 7546, NA, NA, NA, NA, NA, NA, N...
## $ totalTestsAntigen <dbl> NA, NA, 21856, NA, NA, NA, NA, NA, NA, ...
## $ positiveTestsAntigen <dbl> NA, NA, 3300, NA, NA, NA, NA, NA, NA, N...
## $ fips <chr> "02", "01", "05", "60", "04", "06", "08...
## $ positiveIncrease <dbl> 284, 3852, 1337, 0, 976, 6141, 1373, 67...
## $ negativeIncrease <dbl> 3753, 8105, 10920, 0, 11984, 118382, 85...
## $ total <dbl> 552746, 1314774, 1274297, 1616, 1686863...
## $ totalTestResultsIncrease <dbl> 4037, 9078, 11967, 0, 12907, 124523, 22...
## $ posNeg <dbl> 552746, 1314774, 1274297, 1616, 1686863...
## $ deathIncrease <dbl> 0, 16, 10, 0, 6, 73, 4, 8, 0, 8, 74, 37...
## $ hospitalizedIncrease <dbl> 0, 147, 152, 0, 29, 0, 98, 0, 0, 0, 190...
## $ hash <chr> "1b73fb16c097754ce2024af0e49f9d9514248a...
## $ commercialScore <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...
## $ negativeRegularScore <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...
## $ negativeScore <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...
## $ positiveScore <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...
## $ score <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...
## $ grade <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,...
##
##
## Control totals - note that validState other than TRUE will be discarded
##
## # A tibble: 2 x 6
## validState cases deaths hosp tests n
## <lgl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 FALSE 66550 883 NA 470683 1110
## 2 TRUE 8382570 214878 NA 129766502 11991
## Rows: 11,991
## Columns: 6
## $ date <date> 2020-10-23, 2020-10-23, 2020-10-23, 2020-10-23, 2020-10-23,...
## $ state <chr> "AK", "AL", "AR", "AZ", "CA", "CO", "CT", "DC", "DE", "FL", ...
## $ cases <dbl> 284, 3852, 1337, 976, 6141, 1373, 679, 72, 159, 3689, 2224, ...
## $ deaths <dbl> 0, 16, 10, 6, 73, 4, 8, 0, 8, 74, 37, 3, 19, 7, 41, 27, 23, ...
## $ hosp <dbl> 59, 888, 610, 815, 3011, 547, 233, 91, 106, 2114, 1741, 75, ...
## $ tests <dbl> 4037, 9078, 11967, 12907, 124523, 22065, 23481, 6318, 2053, ...
## Rows: 11,991
## Columns: 14
## $ date <date> 2020-01-22, 2020-01-22, 2020-01-23, 2020-01-23, 2020-01-24,...
## $ state <chr> "MA", "WA", "MA", "WA", "MA", "WA", "MA", "WA", "MA", "WA", ...
## $ cases <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...
## $ deaths <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...
## $ hosp <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, ...
## $ tests <dbl> 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, ...
## $ cpm <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...
## $ dpm <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...
## $ hpm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, ...
## $ tpm <dbl> 0.0000000, 0.0000000, 0.1471796, 0.0000000, 0.0000000, 0.000...
## $ cpm7 <dbl> NA, NA, NA, NA, NA, NA, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...
## $ dpm7 <dbl> NA, NA, NA, NA, NA, NA, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...
## $ hpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, ...
## $ tpm7 <dbl> NA, NA, NA, NA, NA, NA, 0.04205130, 0.00000000, 0.06307695, ...## `summarise()` ungrouping output (override with `.groups` argument)## `summarise()` regrouping output by 'date', 'cluster' (override with `.groups` argument)## `summarise()` ungrouping output (override with `.groups` argument)
##
## Recency is defined as 2020-09-24 through current
##
## Recency is defined as 2020-09-24 through current
## `summarise()` regrouping output by 'state', 'cluster', 'date' (override with `.groups` argument)
## `summarise()` ungrouping output (override with `.groups` argument)
## `summarise()` ungrouping output (override with `.groups` argument)
## `summarise()` ungrouping output (override with `.groups` argument)
Updated USA Facts (county-level) data:
# Data downloaded 24-OCT-2020
caseLoc <- "./RInputFiles/Coronavirus/covid_confirmed_usafacts_downloaded_20201024.csv"
deathLoc <- "./RInputFiles/Coronavirus/covid_deaths_usafacts_downloaded_20201024.csv"
# Use existing segments with different data (already downloaded)
old_20201024 <- readRunUSAFacts(maxDate="2020-10-22",
popLoc="./RInputFiles/Coronavirus/covid_county_population_usafacts.csv",
caseLoc=caseLoc,
deathLoc=deathLoc,
dlPop=!file.exists(popLoc),
dlCaseDeath=!file.exists(caseLoc) & !file.exists(deathLoc),
oldFile=cty_20201015_rule9_caseshvy$burdenData,
existingStateClusters=test_old_201023$useClusters,
existingCountyClusters=cty_20201015_rule9_caseshvy$clustVec,
createClusters=FALSE,
orderCluster="cpm"
)##
## -- Column specification --------------------------------------------------------
## cols(
## countyFIPS = col_double(),
## `County Name` = col_character(),
## State = col_character(),
## population = col_double()
## )##
## -- Column specification --------------------------------------------------------
## cols(
## .default = col_double(),
## `County Name` = col_character(),
## State = col_character()
## )
## i Use `spec()` for the full column specifications.## Rows: 878,625
## Columns: 6
## $ countyFIPS <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ countyName <chr> "Statewide Unallocated", "Statewide Unallocated", "State...
## $ state <chr> "AL", "AL", "AL", "AL", "AL", "AL", "AL", "AL", "AL", "A...
## $ stateFIPS <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,...
## $ date <date> 2020-01-22, 2020-01-23, 2020-01-24, 2020-01-25, 2020-01...
## $ cumCases <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...## Warning: `expand_scale()` is deprecated; use `expansion()` instead.##
## -- Column specification --------------------------------------------------------
## cols(
## .default = col_double(),
## `County Name` = col_character(),
## State = col_character()
## )
## i Use `spec()` for the full column specifications.## Rows: 878,625
## Columns: 6
## $ countyFIPS <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ countyName <chr> "Statewide Unallocated", "Statewide Unallocated", "State...
## $ state <chr> "AL", "AL", "AL", "AL", "AL", "AL", "AL", "AL", "AL", "A...
## $ stateFIPS <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,...
## $ date <date> 2020-01-22, 2020-01-23, 2020-01-24, 2020-01-25, 2020-01...
## $ cumDeaths <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...## Warning: `expand_scale()` is deprecated; use `expansion()` instead.## `summarise()` regrouping output by 'source' (override with `.groups` argument)
## `geom_smooth()` using formula 'y ~ x'## `summarise()` ungrouping output (override with `.groups` argument)
## `summarise()` ungrouping output (override with `.groups` argument)##
## Shapes will be created without any floor on the number of cases per million
## Shapes will be created without any floor on the number of deaths per million
## *** Counties with 0 cases/deaths or that fall below the floor for minCase/minDeath ***
## # A tibble: 1 x 4
## cpm_mean_is0 dpm_mean_is0 dpm_mean_ltDeath cpm_mean_ltCase
## <dbl> <dbl> <dbl> <dbl>
## 1 0 0.0138 0 0## `summarise()` ungrouping output (override with `.groups` argument)## `summarise()` regrouping output by 'cluster' (override with `.groups` argument)## `summarise()` ungrouping output (override with `.groups` argument)
## `summarise()` ungrouping output (override with `.groups` argument)## `summarise()` regrouping output by 'date', 'cluster' (override with `.groups` argument)## `summarise()` ungrouping output (override with `.groups` argument)
##
## Recency is defined as 2020-09-23 through current
##
## Recency is defined as 2020-09-23 through current## Warning: Removed 1 rows containing missing values (geom_point).## `summarise()` regrouping output by 'cluster' (override with `.groups` argument)## `summarise()` regrouping output by 'cluster' (override with `.groups` argument)
## `summarise()` regrouping output by 'cluster' (override with `.groups` argument)
## `summarise()` regrouping output by 'cluster' (override with `.groups` argument)
## Warning: `expand_scale()` is deprecated; use `expansion()` instead.
## Joining, by = "fipsCounty"## Joining, by = "fipsCounty"## `summarise()` regrouping output by 'cluster' (override with `.groups` argument)## `summarise()` ungrouping output (override with `.groups` argument)
Updated CDC all-cause deaths data:
# Use data that have previously been downloaded
cdcLoc <- "Weekly_counts_of_deaths_by_jurisdiction_and_age_group_downloaded_20201024.csv"
cdcList_20201024 <- readRunCDCAllCause(loc=cdcLoc,
startYear=2015,
curYear=2020,
weekThru=35,
startWeek=9,
lst=test_old_201023,
epiMap=readFromRDS("epiMonth"),
agePopData=readFromRDS("usPopBucket2020"),
cvDeathThru="2020-08-29",
cdcPlotStartWeek=10,
dlData=FALSE
)## Rows: 177,312
## Columns: 11
## $ Jurisdiction <chr> "Alabama", "Alabama", "Alabama", "Alabama", "A...
## $ `Week Ending Date` <chr> "01/10/2015", "01/17/2015", "01/24/2015", "01/...
## $ `State Abbreviation` <chr> "AL", "AL", "AL", "AL", "AL", "AL", "AL", "AL"...
## $ Year <int> 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015...
## $ Week <int> 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14,...
## $ `Age Group` <chr> "25-44 years", "25-44 years", "25-44 years", "...
## $ `Number of Deaths` <dbl> 67, 49, 55, 59, 47, 59, 41, 47, 59, 57, 54, 50...
## $ `Time Period` <chr> "2015-2019", "2015-2019", "2015-2019", "2015-2...
## $ Type <chr> "Predicted (weighted)", "Predicted (weighted)"...
## $ Suppress <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA...
## $ Note <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA...
## Rows: 177,312
## Columns: 11
## $ Jurisdiction <chr> "Alabama", "Alabama", "Alabama", "Alabama", "Alabama",...
## $ weekEnding <date> 2015-01-10, 2015-01-17, 2015-01-24, 2015-01-31, 2015-...
## $ state <chr> "AL", "AL", "AL", "AL", "AL", "AL", "AL", "AL", "AL", ...
## $ year <int> 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015, ...
## $ week <int> 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16,...
## $ age <chr> "25-44 years", "25-44 years", "25-44 years", "25-44 ye...
## $ deaths <dbl> 67, 49, 55, 59, 47, 59, 41, 47, 59, 57, 54, 50, 58, 42...
## $ period <chr> "2015-2019", "2015-2019", "2015-2019", "2015-2019", "2...
## $ type <chr> "Predicted (weighted)", "Predicted (weighted)", "Predi...
## $ Suppress <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA...
## $ Note <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA...
##
## Check Control Levels and Record Counts for Renamed Data:## `summarise()` ungrouping output (override with `.groups` argument)## # A tibble: 6 x 4
## age n n_deaths_na deaths
## <chr> <int> <int> <dbl>
## 1 25-44 years 26550 1 3241456
## 2 45-64 years 32429 7 12704920
## 3 65-74 years 32421 10 12597720
## 4 75-84 years 32439 12 15661806
## 5 85 years and older 32427 13 20427610
## 6 Under 25 years 21046 0 1398168## `summarise()` regrouping output by 'period', 'year' (override with `.groups` argument)## # A tibble: 12 x 6
## # Groups: period, year [6]
## period year type n n_deaths_na deaths
## <chr> <int> <chr> <int> <int> <dbl>
## 1 2015-2019 2015 Predicted (weighted) 15286 0 5416401
## 2 2015-2019 2015 Unweighted 15286 0 5416401
## 3 2015-2019 2016 Predicted (weighted) 15366 0 5483774
## 4 2015-2019 2016 Unweighted 15366 0 5483774
## 5 2015-2019 2017 Predicted (weighted) 15318 0 5643347
## 6 2015-2019 2017 Unweighted 15318 0 5643347
## 7 2015-2019 2018 Predicted (weighted) 15306 0 5698014
## 8 2015-2019 2018 Unweighted 15306 0 5698014
## 9 2015-2019 2019 Predicted (weighted) 15318 0 5725535
## 10 2015-2019 2019 Unweighted 15318 0 5725535
## 11 2020 2020 Predicted (weighted) 12087 26 5098636
## 12 2020 2020 Unweighted 12037 17 4998902## `summarise()` regrouping output by 'period' (override with `.groups` argument)## # A tibble: 3 x 5
## # Groups: period [2]
## period Suppress n n_deaths_na deaths
## <chr> <chr> <int> <int> <dbl>
## 1 2015-2019 <NA> 153188 0 5.59e7
## 2 2020 Suppressed (counts highly incomplete, <5~ 43 43 0.
## 3 2020 <NA> 24081 0 1.01e7## `summarise()` regrouping output by 'period' (override with `.groups` argument)## # A tibble: 9 x 5
## # Groups: period [2]
## period Note n n_deaths_na deaths
## <chr> <chr> <int> <int> <dbl>
## 1 2015-20~ <NA> 153188 0 5.59e7
## 2 2020 Data in recent weeks are incomplete. Only~ 19486 8 8.54e6
## 3 2020 Data in recent weeks are incomplete. Only~ 396 0 1.85e5
## 4 2020 Data in recent weeks are incomplete. Only~ 417 33 4.95e4
## 5 2020 Data in recent weeks are incomplete. Only~ 1415 2 3.41e5
## 6 2020 Data in recent weeks are incomplete. Only~ 48 0 2.27e4
## 7 2020 Estimates for Pennsylvania are too low fo~ 48 0 2.26e4
## 8 2020 Weights may be too low to account for und~ 260 0 6.28e4
## 9 2020 <NA> 2054 0 8.74e5## `summarise()` regrouping output by 'state' (override with `.groups` argument)## state Jurisdiction n n_deaths_na deaths
## 1 US United States 3612 0 32896397
## 2 CA California 3612 0 3131307
## 3 FL Florida 3612 0 2405576
## 4 TX Texas 3612 0 2365793
## 5 PA Pennsylvania 3612 0 1578266
## 6 OH Ohio 3612 0 1421423
## 7 IL Illinois 3612 0 1241647
## 8 NY New York 3612 0 1175742
## 9 MI Michigan 3612 0 1131859
## 10 NC North Carolina 3552 24 1064138
## 11 GA Georgia 3612 0 986734
## 12 NJ New Jersey 3605 0 881028
## 13 TN Tennessee 3612 0 858799
## 14 VA Virginia 3612 0 789034
## 15 IN Indiana 3609 0 764456
## 16 MO Missouri 3608 0 743370
## 17 AZ Arizona 3612 0 697555
## 18 MA Massachusetts 3578 0 696443
## 19 YC New York City 3608 0 680914
## 20 WA Washington 3612 0 657229
## 21 AL Alabama 3609 0 610495
## 22 WI Wisconsin 3594 0 603682
## 23 MD Maryland 3606 0 581008
## 24 SC South Carolina 3610 0 572934
## 25 KY Kentucky 3577 0 556222
## 26 LA Louisiana 3601 0 537196
## 27 MN Minnesota 3570 0 513356
## 28 CO Colorado 3610 0 455202
## 29 OK Oklahoma 3602 0 454076
## 30 OR Oregon 3442 0 421605
## 31 MS Mississippi 3547 0 371496
## 32 AR Arkansas 3508 0 368851
## 33 CT Connecticut 3170 13 362538
## 34 IA Iowa 3246 0 346523
## 35 PR Puerto Rico 3323 0 338198
## 36 KS Kansas 3304 0 302500
## 37 NV Nevada 3352 0 295131
## 38 WV West Virginia 3052 2 255070
## 39 UT Utah 3498 0 218125
## 40 NM New Mexico 3190 0 209202
## 41 NE Nebraska 2896 0 191890
## 42 ME Maine 2694 0 163366
## 43 ID Idaho 2816 0 156090
## 44 NH New Hampshire 2718 0 137497
## 45 HI Hawaii 2608 0 126806
## 46 RI Rhode Island 2518 4 116260
## 47 MT Montana 2600 0 111945
## 48 DE Delaware 2609 0 101539
## 49 SD South Dakota 2494 0 88395
## 50 ND North Dakota 2479 0 76818
## 51 DC District of Columbia 2592 0 65324
## 52 VT Vermont 2388 0 63168
## 53 WY Wyoming 2364 0 48337
## 54 AK Alaska 2397 0 43125## `summarise()` regrouping output by 'year' (override with `.groups` argument)## Rows: 177,312
## Columns: 11
## $ Jurisdiction <chr> "Alabama", "Alabama", "Alabama", "Alabama", "Alabama",...
## $ weekEnding <date> 2015-01-10, 2015-01-17, 2015-01-24, 2015-01-31, 2015-...
## $ state <chr> "AL", "AL", "AL", "AL", "AL", "AL", "AL", "AL", "AL", ...
## $ year <fct> 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015, ...
## $ week <int> 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16,...
## $ age <fct> 25-44 years, 25-44 years, 25-44 years, 25-44 years, 25...
## $ deaths <dbl> 67, 49, 55, 59, 47, 59, 41, 47, 59, 57, 54, 50, 58, 42...
## $ period <fct> 2015-2019, 2015-2019, 2015-2019, 2015-2019, 2015-2019,...
## $ type <chr> "Predicted (weighted)", "Predicted (weighted)", "Predi...
## $ Suppress <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA...
## $ Note <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA...
## Rows: 86,965
## Columns: 11
## $ Jurisdiction <chr> "Alabama", "Alabama", "Alabama", "Alabama", "Alabama",...
## $ weekEnding <date> 2015-01-10, 2015-01-17, 2015-01-24, 2015-01-31, 2015-...
## $ state <chr> "AL", "AL", "AL", "AL", "AL", "AL", "AL", "AL", "AL", ...
## $ year <fct> 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015, ...
## $ week <int> 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16,...
## $ age <fct> 25-44 years, 25-44 years, 25-44 years, 25-44 years, 25...
## $ deaths <dbl> 67, 49, 55, 59, 47, 59, 41, 47, 59, 57, 54, 50, 58, 42...
## $ period <fct> 2015-2019, 2015-2019, 2015-2019, 2015-2019, 2015-2019,...
## $ type <chr> "Predicted (weighted)", "Predicted (weighted)", "Predi...
## $ Suppress <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA...
## $ Note <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA...
##
##
## *** Data suppression checks ***
## # A tibble: 4 x 11
## Jurisdiction weekEnding state year week age deaths period type Suppress
## <chr> <date> <chr> <fct> <int> <fct> <dbl> <fct> <chr> <chr>
## 1 North Carol~ 2020-08-29 NC 2020 35 45-6~ NA 2020 Pred~ Suppres~
## 2 North Carol~ 2020-08-29 NC 2020 35 65-7~ NA 2020 Pred~ Suppres~
## 3 North Carol~ 2020-08-29 NC 2020 35 75-8~ NA 2020 Pred~ Suppres~
## 4 North Carol~ 2020-08-29 NC 2020 35 85 y~ NA 2020 Pred~ Suppres~
## # ... with 1 more variable: Note <chr>
##
## Data suppression checks OK - 4 records in current week/year suppressed## `summarise()` regrouping output by 'Jurisdiction', 'weekEnding', 'state', 'year', 'week', 'age', 'period', 'type' (override with `.groups` argument)## Rows: 81,789
## Columns: 12
## $ Jurisdiction <chr> "Alabama", "Alabama", "Alabama", "Alabama", "Alabama",...
## $ weekEnding <date> 2015-01-10, 2015-01-10, 2015-01-10, 2015-01-10, 2015-...
## $ state <chr> "AL", "AL", "AL", "AL", "AL", "AL", "AL", "AL", "AL", ...
## $ year <fct> 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015, ...
## $ week <int> 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, ...
## $ age <fct> Under 25 years, 25-44 years, 45-64 years, 65-74 years,...
## $ period <fct> 2015-2019, 2015-2019, 2015-2019, 2015-2019, 2015-2019,...
## $ type <chr> "Predicted (weighted)", "Predicted (weighted)", "Predi...
## $ Suppress <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA...
## $ n <int> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...
## $ deaths <dbl> 25, 67, 253, 202, 272, 320, 28, 49, 256, 222, 253, 332...
## $ Note <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA...
##
## First duplicate is in row number (0 means no duplicates): 0## `summarise()` ungrouping output (override with `.groups` argument)## `summarise()` regrouping output by 'cluster' (override with `.groups` argument)## `summarise()` ungrouping output (override with `.groups` argument)
## `summarise()` regrouping output by 'year' (override with `.groups` argument)
## `summarise()` regrouping output by 'year', 'week' (override with `.groups` argument)
## `summarise()` regrouping output by 'year', 'week' (override with `.groups` argument)
## `summarise()` regrouping output by 'year', 'age', 'week' (override with `.groups` argument)
## `summarise()` regrouping output by 'year' (override with `.groups` argument)
## `summarise()` ungrouping output (override with `.groups` argument)
## `summarise()` regrouping output by 'year' (override with `.groups` argument)
## `summarise()` regrouping output by 'year' (override with `.groups` argument)
## `summarise()` regrouping output by 'year' (override with `.groups` argument)
## `summarise()` regrouping output by 'year' (override with `.groups` argument)
## `summarise()` regrouping output by 'year' (override with `.groups` argument)
## `summarise()` regrouping output by 'year' (override with `.groups` argument)## `summarise()` regrouping output by 'year' (override with `.groups` argument)
## `summarise()` regrouping output by 'year' (override with `.groups` argument)
## `summarise()` regrouping output by 'year' (override with `.groups` argument)
## `summarise()` regrouping output by 'year' (override with `.groups` argument)
## `summarise()` regrouping output by 'year' (override with `.groups` argument)
## `summarise()` regrouping output by 'year' (override with `.groups` argument)
## `summarise()` regrouping output by 'year' (override with `.groups` argument)
## `summarise()` regrouping output by 'year' (override with `.groups` argument)
## `summarise()` regrouping output by 'year' (override with `.groups` argument)
## `summarise()` regrouping output by 'year' (override with `.groups` argument)
## `summarise()` regrouping output by 'year' (override with `.groups` argument)
## `summarise()` regrouping output by 'year' (override with `.groups` argument)
## `summarise()` regrouping output by 'year' (override with `.groups` argument)
## `summarise()` regrouping output by 'year' (override with `.groups` argument)
## `summarise()` regrouping output by 'year' (override with `.groups` argument)
## `summarise()` regrouping output by 'year' (override with `.groups` argument)
## `summarise()` regrouping output by 'year' (override with `.groups` argument)
## `summarise()` regrouping output by 'year' (override with `.groups` argument)
## `summarise()` regrouping output by 'year' (override with `.groups` argument)
## `summarise()` regrouping output by 'year' (override with `.groups` argument)
## `summarise()` regrouping output by 'year' (override with `.groups` argument)
## `summarise()` regrouping output by 'year' (override with `.groups` argument)
## `summarise()` regrouping output by 'year' (override with `.groups` argument)
## `summarise()` regrouping output by 'year' (override with `.groups` argument)
## `summarise()` regrouping output by 'year' (override with `.groups` argument)
## `summarise()` regrouping output by 'year' (override with `.groups` argument)
## `summarise()` regrouping output by 'year' (override with `.groups` argument)
## `summarise()` regrouping output by 'year' (override with `.groups` argument)
## `summarise()` regrouping output by 'year' (override with `.groups` argument)
## `summarise()` regrouping output by 'year' (override with `.groups` argument)
## `summarise()` regrouping output by 'year' (override with `.groups` argument)
## `summarise()` regrouping output by 'year' (override with `.groups` argument)
## `summarise()` regrouping output by 'year' (override with `.groups` argument)
## `summarise()` regrouping output by 'year' (override with `.groups` argument)
## `summarise()` regrouping output by 'year' (override with `.groups` argument)
## `summarise()` regrouping output by 'year' (override with `.groups` argument)
## `summarise()` regrouping output by 'year' (override with `.groups` argument)
## `summarise()` regrouping output by 'year' (override with `.groups` argument)
## `summarise()` regrouping output by 'year' (override with `.groups` argument)
## `summarise()` regrouping output by 'year' (override with `.groups` argument)
## `summarise()` regrouping output by 'year' (override with `.groups` argument)
## `summarise()` regrouping output by 'year' (override with `.groups` argument)
## `summarise()` regrouping output by 'year' (override with `.groups` argument)
## `summarise()` regrouping output by 'year' (override with `.groups` argument)
## `summarise()` regrouping output by 'year' (override with `.groups` argument)
## `summarise()` regrouping output by 'year' (override with `.groups` argument)
## `summarise()` regrouping output by 'year' (override with `.groups` argument)
## `summarise()` regrouping output by 'year' (override with `.groups` argument)
## `summarise()` regrouping output by 'year' (override with `.groups` argument)
## `summarise()` regrouping output by 'year' (override with `.groups` argument)## `summarise()` regrouping output by 'state', 'quarter', 'month' (override with `.groups` argument)## `summarise()` regrouping output by 'state' (override with `.groups` argument)
## `summarise()` regrouping output by 'state' (override with `.groups` argument)
## `summarise()` ungrouping output (override with `.groups` argument)## Joining, by = "state"
## `summarise()` regrouping output by 'year' (override with `.groups` argument)
## `summarise()` regrouping output by 'year' (override with `.groups` argument)
## `summarise()` regrouping output by 'year' (override with `.groups` argument)
## `summarise()` regrouping output by 'year' (override with `.groups` argument)
## `summarise()` regrouping output by 'year' (override with `.groups` argument)
## `summarise()` regrouping output by 'year' (override with `.groups` argument)
## `summarise()` regrouping output by 'age', 'quarter', 'month' (override with `.groups` argument)## `summarise()` regrouping output by 'age' (override with `.groups` argument)
## `summarise()` ungrouping output (override with `.groups` argument)
At a glance, findings are in line with those from earlier in the month. Next steps are to create a new version that includes the cleaned versions of the functions and appropriately saved analysis files.